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1
Glackin, Cornelius. "Fuzzy spiking neural networks." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505831.
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Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.
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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks.
Effective routing algorithms use various techniques to determine the most appropriate route for transmitting data. Determining the best route through a wide area network (WAN), requires the routing algorithm to obtain information concerning all of the nodes, links, and devices present on the network. The most relevant routing information involves various measures that are often obtained in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning is a natural method to employ in an improved routing scheme. The neural network is deemed as a suitable accompaniment because it maintains the ability to learn in dynamic situations. Once the neural network is initially designed, any alterations in the computer routing environment can easily be learned by this adaptive artificial intelligence method. The capability to learn and adapt is essential in today's rapidly growing and changing computer networks. These techniques, fuzzy reasoning and neural networks, when combined together provide a very effective routing algorithm for computer networks.
Computer simulation is employed to prove the new fuzzy routing algorithm outperforms the Shortest Path First (SPF) algorithm in most computer network situations. The benefits increase as the computer network migrates from a stable network to a more variable one. The advantages of applying this fuzzy routing algorithm are apparent when considering the dynamic nature of modern computer networks.<br>Ph. D.
3
Pirovolou, Dimitrios K. "The tracking problem using fuzzy neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14824.
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Frayman, Yakov, and mikewood@deakin edu au. "Fuzzy neural networks for control of dynamic systems." Deakin University. School of Computing and Mathematics, 1999. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.145550.
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This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty.
Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective.
A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated.
Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior.
In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.
5
Leng, Gang. "Algorithmic developments for self-organising fuzzy neural networks." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405165.
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RENTERIA, ALEXANDRE ROBERTO. "TRAFFIC CONTROL THROUGH FUZZY LOGIC AND NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2695@1.
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FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>Este trabalho apresenta a utilização de lógica fuzzy e de
redes neurais no desenvolvimento de um controlador de
semáforos - o FUNNCON. O trabalho realizado consiste em
quatro etapas principais: estudo dos fundamentos de
engenharia de tráfego; definição de uma metodologia para a
avaliação de cruzamentos sinalizados; definição do
modelo do controlador proposto; e implementação com dados
reais em um estudo de caso.O estudo sobre os fundamentos de
engenharia de tráfego aborda a definição de termos,os
parâmetros utilizados na descrição dos fluxos de tráfego,
os tipos de cruzamentos e seus semáforos, os sistemas de
controle de tráfego mais utilizados e as diversas medidas de
desempenho.Para se efetuar a análise dos resultados do
FUNNCON, é definida uma metodologia para a avaliação de
controladores. Apresenta-se, também, uma investigação sobre
simuladores de tráfego existentes, de modo a permitir a
escolha do mais adequado para o presente estudo. A
definição do modelo do FUNNCON compreende uma descrição
geral dos diversos módulos que o compõem. Em seguida, cada
um destes módulos é estudado separadamente: o uso de redes
neurais para a predição de tráfego futuro; a elaboração de
um banco de cenários ótimos através de um otimizador; e a
criação de regras fuzzy a partir deste banco.No estudo de
caso, o FUNNCON é implementado com dados reais fornecidos
pela CET-Rio em um cruzamento do Rio de Janeiro e comparado
com o controlador existente.É constatado que redes neurais
são capazes de fornecer bons resultados na predição do
tráfego futuro. Também pode ser observado que as regras
fuzzy criadas a partir do banco de cenários ótimos
proporcionam um controle efetivo do tráfego no cruzamento
estudado. Uma comparação entre o desempenho do FUNNCON e o
do sistema atualmente em operação é amplamente favorável ao
primeiro.<br>This work presents the use of fuzzy logic and neural
networks in the development of a traffic signal controller -
FUNNCON. The work consists of four main sections: study of
traffic engineering fundamentals; definition of a
methodology for evaluation of traffic controls; definition
of the proposed controller model; and implementation on a
case study using real data.The study of traffic engineering
fundamentals considers definitions of terms,parameters used
for traffic flow description, types of intersections and
their traffic signals,commonly used traffic control systems
and performance measures.In order to analyse the results
provided by FUNNCON, a methodology for the evaluation of
controllers is defined. The existing traffic simulators are
investigated, in order to select the best one for the
present study.The definition of the FUNNCON model includes
a brief description of its modules.Thereafter each module
is studied separately: the use of neural networks for
future traffic prediction; the setup of a best scenario
database using an optimizer; and the extraction of
fuzzy rules from this database.In the case study, FUNNCON
is implemented with real data supplied by CET-Rio
from an intersection in Rio de Janeiro; its performance is
compared with that of the existing controller.It can be
observed that neural networks can present good results in
the prediction of future traffic and that the fuzzy rules
created from the best scenario database lead to an
effective traffic control at the considered intersection.
When compared with the system in operation, FUNNCON reveals
itself much superior.
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Kim, Hung-man. "Implementing adaptive fuzzy logic controllers with neural networks." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187160.
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The goal of intelligent control is to achieve control objectives for complex systems where it is impossible or infeasible to develop a mathematical system model but expert skills and heuristic knowledge from human experiences are available for control purposes. To this end, an intelligent control system must have the essential characteristics of human control experiences, i.e., linguistic knowledge representation, which facilitates the process of knowledge acquisition and transfer, and adaptive knowledge evolution or learning, which leads to the improvement in system performance and knowledge. This dissertation presents an efficient approach that combines fuzzy logic and neural networks to capture these two important features required for an intelligent control system. A design method for adaptive neuro-fuzzy controllers has been proposed using structured neuro-fuzzy networks. The structured neuro-fuzzy networks consist of three types of subnets for pattern recognition, fuzzy reasoning, and control synthesis, respectively. Each subnet is constructed directly from the decision-making procedure of fuzzy logic based control systems. In this way, a one-to-one mapping between a fuzzy logic based control system and a structured neuro-fuzzy network is established. This mapping enables us to create a knowledge structure within neural networks based on fuzzy logic, and to give a learning ability to fuzzy controls using neural networks. From the perspective of neural networks, the proposed design method offers a mechanism to: construct networks with heuristic knowledge, instead of using digital training pairs, which are much more difficult to get, build decision structures into networks, which divide a network into several functional regions and make the network no longer just as a black-box function approximator, and conduct network learning in a distributed fashion, i.e., each sub-network of different functional regions can learn its own function independently. On the other hand, from the perspective of fuzzy logic, the proposed design method provides a tool to: refine membership functions, inference procedures, and defuzzification algorithms of fuzzy control systems; generate new fuzzy control rules so that fuzzy control systems can adapt to gradual changes in environments and implement parallel execution of rule matching, firing, and defuzzification. Several simulation studies have been conducted to demonstrate the use of the structured neuro-fuzzy networks. The effectiveness of the proposed design method has been clearly shown by the results of these studies. These results have also indicated that fuzzy logic and neural networks are complementary and their combination is ideal to achieve the goal of intelligent control.
8
Gabrys, Bogdan. "Neural network based decision support : modelling and simulation of water distribution networks." Thesis, Nottingham Trent University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387534.
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Bordignon, Fernando Luis. "Aprendizado extremo para redes neurais fuzzy baseadas em uninormas." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259061.
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Orientador: Fernando Antônio Campos Gomide<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-22T00:50:20Z (GMT). No. of bitstreams: 1
Bordignon_FernandoLuis_M.pdf: 1666872 bytes, checksum: 4d838dfb4ec418698d9ecd3b74e7c981 (MD5)
Previous issue date: 2013<br>Resumo: Sistemas evolutivos são sistemas com alto nível de adaptação capazes de modificar simultaneamente suas estruturas e parâmetros a partir de um fluxo de dados, recursivamente. Aprendizagem a partir de fluxos de dados é um problema contemporâneo e difícil devido à taxa de aumento da dimensão, tamanho e disponibilidade temporal de dados, criando dificuldades para métodos tradicionais de aprendizado. Esta dissertação, além de apresentar uma revisão da literatura de sistemas evolutivos e redes neurais fuzzy, aborda uma estrutura e introduz um método de aprendizagem evolutivo para treinar redes neurais híbridas baseadas em uninormas, usando conceitos de aprendizado extremo. Neurônios baseados em uninormas fundamentados nas normas e conormas triangulares generalizam neurônios fuzzy. Uninormas trazem flexibilidade e generalidade a modelos neurais fuzzy, pois elas podem se comportar como normas triangulares, conormas triangulares, ou de forma intermediária por meio do ajuste de elementos identidade. Este recurso adiciona uma forma de plasticidade em modelos de redes neurais. Um método de agrupamento recursivo para granularizar o espaço de entrada e um esquema baseado no aprendizado extremo compõem um algoritmo para treinar a rede neural. _E provado que uma versão estática da rede neural fuzzy baseada em uninormas aproxima funções contínuas em domínios compactos, ou seja, _e um aproximador universal. Postula-se, e experimentos computacionais endossam, que a rede neural fuzzy evolutiva compartilha capacidade de aproximação equivalente, ou melhor, em ambientes dinâmicos, do que as suas equivalentes estáticas<br>Abstract: Evolving systems are highly adaptive systems able to simultaneously modify their structures and parameters from a stream of data, online. Learning from data streams is a contemporary and challenging issue due to the increasing rate of the size and temporal availability of data, turning the application of traditional learning methods limited. This dissertation, in addition to reviewing the literature of evolving systems and neuro fuzzy networks, addresses a structure and introduces an evolving learning approach to train uninorm-based hybrid neural networks using extreme learning concepts. Uninorm-based neurons, rooted in triangular norms and conorms, generalize fuzzy neurons. Uninorms bring flexibility and generality to fuzzy neuron models as they can behave like triangular norms, triangular conorms, or in between by adjusting identity elements. This feature adds a form of plasticity in neural network modeling. An incremental clustering method is used to granulate the input space, and a scheme based on extreme learning is developed to train the neural network. It is proved that a static version of the uninorm-based neuro fuzzy network approximate continuous functions in compact domains, i.e. it is a universal approximator. It is postulated and computational experiments endorse, that the evolving neuro fuzzy network share equivalent or better approximation capability in dynamic environments than their static counterparts<br>Mestrado<br>Engenharia de Computação<br>Mestre em Engenharia Elétrica
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Aimejalii, K., Keshav P. Dahal, and M. Alamgir Hossain. "GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks." IEEE, 2007. http://hdl.handle.net/10454/2553.
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Identification of fuzzy rules is an important issue in
designing of a fuzzy neural network (FNN). However,
there is no systematic design procedure at present. In
this paper we present a genetic algorithm (GA) based
learning algorithm to make use of the known membership
function to identify the fuzzy rules form a large set
of all possible rules. The proposed learning algorithm
initially considers all possible rules then uses the
training data and the fitness function to perform ruleselection.
The proposed GA based learning algorithm
has been tested with two different sets of training data.
The results obtained from the experiments are promising
and demonstrate that the proposed GA based
learning algorithm can provide a reliable mechanism
for fuzzy rule selection.
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Karaboga, Dervis. "Design of fuzzy logic controllers using genetic algorithms." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296639.
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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.
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Nejatali, Abdolhossein. "Electrical impedance tomography with neural networks and fuzzy sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23645.pdf.
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Dias, De Macedo Filho Antonio. "Microwave neural networks and fuzzy classifiers for ES systems." Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244066.
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Hsu, Cheng-Yu. "Condition monitoring of fluid power systems using artificial neural networks." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295443.
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Vetcha, Sarat Babu. "Fault diagnosis in pumps by unsupervised neural networks." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300604.
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González, Marek. "Fuzzy neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234941.
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This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
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Nukala, Ramesh Babu. "Neuro-fuzzy controllers for unstable systems." Thesis, Lancaster University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364362.
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Ji, Wei. "Artificial neural networks and fuzzy systems in bladder cancer prognosis." Thesis, Coventry University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417616.
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Rodriguez, Carlos Alberto Ramirez. "Fuzzy neural networks for classsification problems with uncertain data input." Thesis, University of Surrey, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336530.
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MACHADO, MARIA AUGUSTA SOARES. "IDENTIFICATION OF NON-SEASONAL TIME SERIES THROUGH FUZZY NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7554@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Observando a dificuldade de batimento (match) dos padrões
de comportamento das funções de autocorrelação e de
autocorrelação parcial teóricas com as respectivas funções
e as autocorrelação e de autocorrelação parcial estimadas
de uma séries temporal, aliada ao fato da dificuldade em
definir um número em específico como delimitador
inequívoco do que seja um lag significativo, tornam clara
a dose de julgamento subjetivo a ser realizado por um
especialista de análise de séries temporais na tomada de
decisão sobre a estrutura de Box & Jenkins adequada a ser
escolhida para modelar o processo estocástico sendo
estudado. A matemática nebulosa permite a criação de
sistemas de inferências nebulosas (inferência dedutiva) e
representa o conhecimento de forma explícita, através de
regras nebulosas, possibilitando, facilmente, o
entendimento do sistema em estudo. Por outro lado, um
modelo de redes neurais representa o conhecimento de forma
implícita, adquirido através de exemplos (dados),
possuindo excelente capacidade de generalização
(inferência indutiva). Esta tese apresenta um sistema
especialista composto de cinco redes neurais nebulosas do
tipo retropropagação para o auxílio na análise de séries
temporais não sazonais. O sistema indica ao usuário a
estrutura mais adequada, dentre as estruturas AR(1), MA
(1), AR(2), MA(2) e ARMA(1,1), tomando como base a menor
distância Euclidiana entre os valores esperados e as
saídas das redes neurais nebulosas.<br>It is well known the difficulties associated with the
tradicional procedure for model identification of the Box
& Jenkins model through the pattern matching of the
theoretical and estimated ACF and PACF. The decision on
the acceptance of the null hypothesis of zero ACF (or
PACF) for a given lag is based on a strong asymptotic
result, particularly for the PACF, leading, sometimes, to
wrong decisions on the identified order of the models.
The fuzzy logic allows one to infer system governed by
incomplete or fuzzy knowledge (deductive inference) using
a staighforward formulation of the problem via fuzzy
mathematics. On the other hand, the neural network
represent the knowledge in a implicit manner and has a
great generalization capacity (inductive inference).
In this thesis we built a specialist system composed of 5
fuzzy neural networks to help on the automatic
identificationof the following Box & Jenkins ARMA
structure AR(1), MA(1), AR(2), MA(2) and ARMA (1,1),
through the Euclidian distance between the estimated
output of the net and the corresponding patterns of each
one of the five structures.
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Ramirez-Rodriguez, Carolos Alberto. "Fuzzy neural networks for classification problems with uncertain data input." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/843376/.
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This thesis addresses the problem of classification with uncertain input data using fuzzy neural networks. Uncertainty in classification is produced, in most cases, by overlapping among classes due to noise in the input data. However, there are many examples of classification problems where the classes overlap naturally. Conventional classifier design requires the model to arrive to a crisp decision by minimising the probability of misclassification. A decision surface is fitted and a certain compromise is reached in order to artificially separate the overlapping classes. This study suggests that a better approach is to design a classifier capable of deciding which class is most representative of an unknown input pattern and also of signalling whether that pattern belongs to an area of overlap between two classes. In this way, uncertain patterns can be isolated and subject to further analysis. This approach is implemented here through a hierarchical fuzzy-neural system (HFNS) that combines backpropagation neural networks (BNNs) and fuzzy logic techniques. The main feature of the HFNS is its ability to identify patterns belonging to more than one class and send them to a second level of processing for a more exhaustive classification. The HFNS is developed after a detailed analysis and an experimental comparison of various proposed fuzzy-neural models. First, the interval backpropagation neural network is investigated. This model allows the use of both linguistic and numerical information to be used as input to the network. The interval BNN proves to be a good alternative to the design of fuzzy systems (FS) when there is a small amount of rules and when numerical data is available. The ability of the HFNS to detect ambiguous patterns is investigated by replacing with fuzzy partitions, the hard partitions used to label the training data. Two new algorithms for fuzzification of labelling data used for training BNNs are proposed. The algorithms precisely represent the degree of similarity of a training pattern to the different class templates involved in a classification problem. BNN trained using the proposed fuzzy labels improve their ability to detect areas of overlapping among the classes as compared with conventional BNN. In a case study, BNNs trained using the proposed algorithm are applied to the detection of atrial fibrillation episodes in records of the MIT-BIH Database with an average classification rate of 87%. The component blocks of the HFNS are trained using a fuzzy neural model which automatically adjusts the learning rate and the slope of the sigmoid function in the backpropagation algorithm. The model is based on fuzzy associative memories. The aim of this integration is to accelerate the training stage of BNN. It is shown that the fuzzy control of the BNN learning rate decreases the number of training interactions required for reaching convergence. In relation to the fuzzy control of the steepness factor of the sigmoid function, no significant effect is found other than the scaling of the learning rate parameter. The HFNS successfully integrates neural networks and fuzzy logic in a new classification system which outperforms conventional methods in the management of uncertainty at different levels. The HFNS is successfully applied to the classification of anomalous electrocardiogram patterns in a selected record of the MIT-BIT ECG Database with classification rates up to 98%.
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Tripathi, Nishith D. "Generic Adaptive Handoff Algorithms Using Fuzzy Logic and Neural Networks." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29267.
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Efficient handoff algorithms cost-effectively enhance the capacity and Quality of Service (QoS) of cellular systems. This research presents novel approaches for the design of high performance handoff algorithms that exploit attractive features of several existing algorithms, provide adaptation to dynamic cellular environment, and allow systematic tradeoffs among different system characteristics. A comprehensive foundation of handoff and related issues of cellular communications is given. The tools of artificial intelligence utilized in this research, neural networks and fuzzy logic, are introduced. The scope of existing simulation models for macrocellular and microcellular handoff algorithms is enhanced by incorporating several important features. New simulation models suitable for performance evaluation of soft handoff algorithms and overlay handoff algorithms are developed. Four basic approaches for the development of high performance algorithms are proposed and are based on fuzzy logic, neural networks, unified handoff candidate selection, and pattern classification. The fuzzy logic based approach allows an organized tuning of the handoff parameters to provide a balanced tradeoff among different system characteristics. The neural network based approach suggests neural encoding of the fuzzy logic systems to simultaneously achieve the goals of high performance and reduced complexity. The unified candidacy based approach recommends the use of a unified handoff candidate selection criterion to select the best handoff candidate under given constraints. The pattern classification based approach exploits the capability of fuzzy logic and neural networks to obtain an efficient architecture of an adaptive handoff algorithm. New algorithms suitable for microcellular systems, overlay systems, and systems employing soft handoff are described. A basic adaptive algorithm suitable for a microcellular environment is proposed. Adaptation to traffic, interference, and mobility has been superimposed on the basic generic algorithm to develop another microcellular algorithm. An adaptive overlay handoff algorithm that allows a systematic balance among the design parameters of an overlay system is proposed. Important considerations for soft handoff are discussed, and adaptation mechanisms for new soft handoff algorithms are developed.<br>Ph. D.
24
Jin, Y. "Intelligent neural control and its applications in robotics." Thesis, University of the West of England, Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240830.
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Canuto, Anne Magaly de Paula. "Combining neural networks and fuzzy logic for applications in character recognition." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.344107.
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Styliandidis, Orestis. "Knowledge from data : concept induction using fuzzy and neural methods." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361076.
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Wu, Tzung-Han, and 吳宗翰. "Study on Ramsay Fuzzy Neural Networks." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/8548kw.
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碩士<br>國立中山大學<br>電機工程學系研究所<br>96<br>In this thesis, M-estimators with Ramsay’s function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.
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Chen, Shih-Chieh, and 陳士傑. "Fuzzy modelling using hybrid neural networks." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/03622777357915110698.
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碩士<br>國立中央大學<br>機械工程學系<br>84<br>A neural-network-based structure learning fuzzy controller is
proposed.The consequent of a rule is assumed to be a linear
combination of thefuzzy sets associated with an output
linguistic variable as against thetraditional fuzzy rules whose
consequents are decided by an experiencedoperator. The
defuzzified result of these proposed fuzzy rules is provedto
conform with the general meaning of a defuzzifier and is shown
to berealizable through a neural network in which the
coefficients associatedwith the linear combination are tuned
from examples. The proposed fuzzyrules are also shown to be
robust with respect to any reasonable changesin the antecedents,
which means the parameter learning of the antecedentsis
unnecessary in our scheme. Lastly, the capability of learning
of theproposed fuzzy logic controller is demonstrated through
examples.
29
Wu, Hsu-Kun, and 吳旭焜. "Research on Robust Fuzzy Neural Networks." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/24109251503970382326.
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博士<br>國立中山大學<br>電機工程學系研究所<br>99<br>In many practical applications, it is well known that data collected inevitably contain one or more anomalous outliers; that is, observations that are well separated from the majority or bulk of the data, or in some fashion deviate from the general pattern of the data. The occurrence of outliers may be due to misplaced decimal points, recording errors, transmission errors, or equipment failure. These outliers can lead to erroneous parameter estimation and consequently affect the correctness and accuracy of the model inference. In order to solve these problems, three robust fuzzy neural networks (FNNs) will be proposed in this dissertation. This provides alternative learning machines when faced with general nonlinear learning problems. Our emphasis will be put particularly on the robustness of these learning machines against outliers. Though we consider only FNNs in this study, the extension of our approach to other neural networks, such as artificial neural networks and radial basis function networks, is straightforward.
In the first part of the dissertation, M-estimators, where M stands for maximum likelihood, frequently used in robust regression for linear parametric regression problems will be generalized to nonparametric Maximum Likelihood Fuzzy Neural Networks (MFNNs) for nonlinear regression problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) will be derived.
In the second part of the dissertation, least trimmed squares estimators, abbreviated as LTS-estimators, frequently used in robust (or resistant) regression for linear parametric regression problems will be generalized to nonparametric least trimmed squares fuzzy neural networks, abbreviated as LTS-FNNs, for nonlinear regression problems. Again, simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided.
In the last part of the dissertation, by combining the easy interpretability of the parametric models and the flexibility of the nonparametric models, semiparametric fuzzy neural networks (semiparametric FNNs) and semiparametric Wilcoxon fuzzy neural networks (semiparametric WFNNs) will be proposed. The corresponding learning rules are based on the backfitting procedure which is frequently used in semiparametric regression.
30
Lee, Chia-Yuan, and 李家源. "Multiple Compensatory Neural Fuzzy Networks Fusion Using Fuzzy Integral." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/vrez32.
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碩士<br>朝陽科技大學<br>資訊工程系碩士班<br>92<br>This thesis presents a novel method for combining multiple compensatory neural fuzzy networks (CNFNs) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complemen each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple CNFN classifiers with fuzzy integral (FI) is proposed to achieve data classification with higher accurate than existing traditional methods. We first apply multiple CNFNs fusion using fuzzy integral based on compensatory degree to establish the classification model. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Experimental results show that the fusion of multiple CNFNs using fuzzy integral can perform better than existing traditional methods.
31
Xue, Kuo Qiang, and 薛國強. "An intelligent sales forecasting system through artificial neural networks and fuzzy neural network." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/07455980576654976365.
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Tsai, Chiachih, and 蔡嘉志. "Applications of Wireless Sensor Networks Based on Fuzzy Neural Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/18594029970285141084.
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博士<br>國防大學理工學院<br>國防科學研究所<br>100<br>Due to immense potential applications, wireless sensor networks (WSNs) have attracted research interests in recent years, including remote environmental monitoring, data fusion, sensing (temperature, pressure, speed) and military applications. This dissertation applies the fuzzy logic and neural network technologies to a monitored area which deployed miniature wireless sensor nodes. With the advantages of inherent accuracy and simplicity, the fuzzy logic and neural network technologies manifests the effectiveness on the environmental monitoring and control applications of wireless sensor networks.
First, we apply the fuzzy technology to control the air-conditioning strength and blade angle of a car conditioner to equalize the comfortable temperature in the front- and rear-seat areas. The wireless nodes equipped with temperature sensor are installed to gather temperature information and then transmit this information to the central control terminal which executes the fuzzy inference control logic. The experiments show that the fuzzy technology would greatly improve the response for the automotive control and smart computation in the wireless sensor network systems.
And then we develop a novel fuzzy logic algorithm to the remote environmental monitoring applications. Through a simple and effective fuzzy logic algorithm, every interesting node in the monitored area can be effectively calculated. This novel algorithm manifests their simplicity and accuracy and its performance characterized by root mean square error is better than the one with the standard Mamadni fuzzy logic method.
Our study focuses on two particular neural network models, back-propagation network (BPN) and general regression neural network (GRNN) for the temperature prediction in a monitored factory. The prediction accuracy of these two models is evaluated by practical monitored data. We found that the model based on GRNN can accelerate the learning speed and rapidly converge to the optimal regression surface with large number of data sets. With the simulation results, we can show that the model based on GRNN effectively improve the predictability of the one based on BPN.
Finally, we combine the genetic algorithm (GA) and the radial basis function (RBF) neural network in study of event detection for factory monitoring. As we know, the center of RBF, the width of RBF and output weight of RBF have a great influence on the performance of RBF neural network. In this study, we apply genetic algorithm to determine these parameters to improve the performance of the event detection. The experiments indicate that the GA-RBF algorithm is better than the traditional BPN and RBF neural network algorithms in both speed and precise of convergence. In this work, we find a responsive and effective algorithm in the WSN applications by integrating fuzzy theory and neural network technology. The combination of fuzzy theory and neural network technology should be a powerful strategy for the various WSN applications.
33
陳俊維. "Fuzzy Neural Networks Based Adaptive Cruise Control." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/01697651922759359482.
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碩士<br>國立交通大學<br>電機與控制工程系<br>90<br>Adaptive Cruise Control (ACC) System is an important part of the Advanced Vehicle Control and Safety System (AVCSS) in Intelligent Transportation Systems (ITS). In this thesis we design an ACC controller based on fuzzy neural networks for following a leading vehicle to achieve the desired safety distance, or cruising at the pre-selected speed. The transmission between the two maneuvers is carried out automatically. The advantage of using fuzzy neural networks is that it doesn’t require the complete knowledge of nonlinear vehicle dynamics, and it can be applied to any vehicle regardless of its nonlinear or unobservable dynamics. We separate the ACC controller into three parts. The first one is used to determine the desired acceleration according to the current traffic situation (relative speed and relative distance) and driver’s driving style, the second one is used to determine the throttle angle or braking command depending on the current vehicle speed and desired acceleration, and the last one is used to compensate the modeling error and disturbances. The performance of ACC controller is evaluated based on a complex traffic model, which includes the accurate nonlinear vehicle dynamic model and various environments, and simulated by a computer with MATLAB software. The vehicle is assumed to be equipped with sensors that can measure the relative distance and vehicle speed. In addition, we also take maximum allowable jerks and the system delay into account. The fuzzy logic and neural networks based controller proposed in this thesis provides a safe, convenient and comfortable driving assistance system. The controller can switch between car following and cruise control automatically.
34
Hong, Shing-Fu, and 洪清富. "VLSI Design of Fuzzy Functional Neural Networks." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/08531360158426516811.
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Tien-Sheng, Tang, and 唐天生. "Fuzzy modelling using self-organizing neural networks." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/97218540581668054505.
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碩士<br>國立中央大學<br>機械工程研究所<br>87<br>A neural-network-based structure learning fuzzy system is proposed. The
consequent of a rule is assumed
to be a linear combination of two fuzzy sets
associated with an output variable as against the traditional
fuzzy rules whose consequents are decided by an experienced operator. The
defuzzified result of these proposed fuzzy rules is proved to conform with the
general meaning of a defuzzifier and is shown to be realizable through a
neural network in which the coefficients associated with the linear
combination are tuned from input-output pairs. To improve its performance
further, we incorporate the proposed system with a self-organizing
heuristic method (SOHM) to generate necessary fuzzy rules
automatically.
Lastly, the capability of this approach is demonstrated through examples.
36
JIAN, YUAN-ZHEN, and 簡源震. "Adaptive fuzzy logic controller using neural networks." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/67432838151022219149.
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Guo-Yin, Chen, and 陳國寅. "On the Study of the Learning Performance for Neural Networks and Neural Fuzzy Networks." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/07825885643934324498.
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碩士<br>國立臺灣科技大學<br>電機工程技術研究所<br>86<br>Neural networks and fuzzy systems can be used to estimate functions frominput-output data pairs and behave as associative memories. Since both approaches are model-free estimators, the resultant systems can be said to directly model the input-output relationship from the given training patterns without requiring other knowledge. As a matter of fact, those two approaches have been proven to be universal approximators under certain circumstances. It is more than often that such a universal property cannot be satisfied in the actual cases due to poor learning capability. In this research, instead in pursuit of approaches to improve the learning schemes, we were aimed at studying the general learning concept and the fundamental differences between those two universal approximators. In this thesis, three kinds of numerical learning systems are discussed. They are neural networks, neural fuzzy systems, and neural fuzzy systems with structure learning. Three systems are used in our study; they are the fuzzy car system, the sinc function approximation, and the terrain location identification system. Those systems represent different kinds of learning problems. The fuzzy car system is to learn from the training data that are very noisy and with non-deterministic input-output relationship. The sinc function system is to learn form an exactly know system, and therefore, the system errors and the added noise magnitude can be defined exactly to evaluate the learning performance of the employed learning mechanisms. The terrain location identification problem on the other hand represents a very complicated learning target. Beside of noisy and non-deterministic training data, the training task must learn from a very large size of training data, which may cause lots of learning problems. In our implementation of the terrain location identification system, several phenomena have been discovered. The first phenomenon is called the fake convergence in our research. In this thesis, a fuzzy hierarchical approach is proposed to resolve the problem. With this fuzzy hierarchical structure, the learning process can become fast and the training errors are also significantly reduced. Another issue in the terrain location identification problem is regarding about embedding domain knowledge into the learning structure of neural fuzzy networks. The domain knowledge is used in a neural fuzzy network in which the TSK fuzzy rule model is equipped. With such inclusion of knowledge, the learning performance is dramatically improved. Finally, with the structure of using domain knowledge, a new way of fusing data other than the traditional Kalman filter type of data fusion is proposed and discussed. Our results have also demonstrate the superiority of our approach to the traditional Kalman filter. In our study of neural fuzzy networks with structure learning, two approaches of tuning the parameters in the linear functions of the consequent part of fuzzy rules can be found in the literature. They are the traditional backpropagation algorithm (BP) and the recursive least square method (RLS). From our implementation, we may say that it is not always a good idea to use the RLS training to replace the BP training.
38
"Learning algorithms for neural networks with fuzzy information." Chinese University of Hong Kong, 1990. http://library.cuhk.edu.hk/record=b5895362.
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by Lee Tan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.<br>Bibliography: leaves [128]-[130]<br>Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1<br>Chapter 1.1 --- Introduction to Artificial Neural Networks --- p.1-4<br>Chapter 1.1.1 --- Fundamentals of Artificial Neural Networks --- p.1-5<br>Chapter 1.1.2 --- Various Artificial Neural Network Models ´ؤA Review --- p.1-11<br>Chapter 1.2 --- Introduction to Fuzzy Sets Theory --- p.1-17<br>Chapter 1.2.1 --- "Fuzziness, Fuzzy sets and Membership Function" --- p.1-17<br>Chapter 1.2.2 --- Applications of Fuzzy Sets --- p.1-19<br>Connective Summary --- p.1-21<br>Chapter CHAPTER 2 --- LEARNING WITH FUZZY INFORMATION --- p.2-1<br>Chapter 2.1 --- "Decision Making, Pattern Associating and Pattern Classification" --- p.2-3<br>Chapter 2.2 --- Artificial Neural Networks as Learning Decision Systems --- p.2-6<br>Chapter 2.3 --- Fuzziness in Decision Making Processes --- p.2-10<br>Chapter 2.4 --- Learning with Fuzzy Information --- p.2-12<br>Chapter 2.5 --- The Formulation of Our Approach --- p.2-16<br>Connective Summary --- p.2-18<br>Chapter CHAPTER 3 --- A MODIFIED BACKPROPAGATION ALGORITHM FOR MULTILAYER FEEDFORWARD NETWORKS --- p.3-1<br>Chapter 3.1 --- Preliminaries --- p.3-3<br>Chapter 3.2 --- The Error Backpropagation Algorithm (EBPA) --- p.3-8<br>Chapter 3.3 --- A Modified EBPA Learning with A Priori Fuzzy Information --- p.3-11<br>Chapter 3.3.1 --- The Membership-Weighed Objective Function --- p.3-11<br>Chapter 3.3.2 --- The Fuzzy Error Backpropagation Algorithm --- p.3-13<br>Chapter 3.4 --- Discussion on the Proposed Fuzzy EBPA --- p.3-15<br>Chapter 3.4.1 --- Methods of Determining Membership Functions --- p.3-15<br>Chapter 3.4.2 --- Fuzzy EBPA Alters the Effective Target Patterns --- p.3-19<br>Chapter 3.4.3 --- Estimating the Learning Rates Required for the Fuzzy EBPA --- p.3-21<br>Connective Summary --- p.3-24<br>Chapter CHAPTER 4 --- APPLICATION EXAMPLES --- p.4-1<br>Chapter 4.1 --- A Single Node Classifier --- p.4-2<br>Chapter 4.2 --- The Fuzzy XOR Problem --- p.4-29<br>Chapter 4.2.1 --- Network Configuration 1 --- p.4-36<br>Chapter 4.2.2 --- Network Configuration 2 --- p.4-46<br>Chapter 4.2.3 --- Comments on the Simulation Results --- p.4-50<br>Chapter 4.3 --- A Speech Recognition System --- p.4-54<br>Connective Summary --- p.4-59<br>Chapter CHAPTER 5 --- DISCUSSION AND CONCLUSION --- p.5-1
39
Chuang, Cheng-Ta, and 莊政達. "RFID Fault Diagnosis by Using Fuzzy Neural Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/b8e4m8.
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碩士<br>靜宜大學<br>資訊管理學系研究所<br>97<br>In recent years, Radio Frequency IDentification system (RFID) is considered the one of top ten technical progresses of this century. And the value of RFID consists in its automation. Therefore, ensuring the reliability of RFID system is the most important task on its application.
Traditionally, system maintenance is based on the artificial experience, but this approach depends on ample experience in maintenance. Therefore, if there is an automatic fault classified system, it will be greatly helpful to enhance the maintenance efficiency.
RFID fault diagnosis belongs to a classification-related research, and neural networks are often used to solve the classification-related issues. In addition, the fuzzy theory gets beyond the traditional concept of “0 and 1”, and is used to analyze semantic strength and avoid semantic ambiguity.
Therefore, this research attempts to combine the fuzzy theory and neural networks in RFID fault diagnosis to assist the maintenance staff eliminating the system fault efficiently.
In this research, we propose an approach that combines a fuzzy neural network and an additional probability diagnostic method. The proposed two-stage model significantly reduces the input units and output units of neural network, and effectively controls the fuzzy neural network learning speed and precision. Most importantly, and the proposed approach makes RFID fault diagnosis more accuracy.
40
Li, Zhi Ren, and 李志仁. "Fingerprint recognition using neural networks and fuzzy theory." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/87999273234260416206.
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Lee, Ching-Hung, and 李慶鴻. "Analysis of Fuzzy Neural Networks and Its Applications." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/53710177302734251220.
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博士<br>國立交通大學<br>電機與控制工程系<br>88<br>In this dissertation, we investigate a fuzzy neural network (FNN)
system that combines the advantages of the fuzzy logic and neural
network systems. The FNN system is a straight-forward
implementation of fuzzy inference system with four layered network
structure. This system combines the advantages of the fuzzy logic
control and neural networks. Base on this FNN system, a recurrent
structure of the FNN (RFNN) are proposed in this dissertation. The
RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN).
Results for the FNN -fuzzy inference engine, universal
approximation, and convergence analysis are extended to the RFNN.
Moreover, the RFNN extends the basic ability of the FNN to cope
with temporal problems. Subsequently, we discuss the relationship
between membership and mapping accuracy of the FNN system. A new
method to fine-tune the Gaussian membership functions of the FNN
is proposed to improve the approximation accuracy which subverts
the commonly used property of membership functions. For
illustrating the effectiveness of our approach, several
applications of the FNN are also presented, including the PID
tuning method based on gain and phase margin specifications,
identification and control of Hammerstein systems, and fuzzy rules
Acknowledgement i
Abstract in Chinese ii
Abstract in English iv
Contents v
List of Figures vi
List of Tables xi
1 Introduction 1
1.1 Introduction and Motivation．．．．．．．．．．．．．．．．．．．．．．．．．． 1
1.2 Research objectives．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．2
1.3 Overview．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．3
1.3.1 Organization of this dissertation ．．．．．．．．．．．．．．．．．．．．．． 3
1.3.2 Overview．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．3
2 Fuzzy Neural Network 6
2.1 Outlines．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 6
2.2 Structure of the fuzzy neural network．．．．．．．．．．．．．．．．．．．．．．．8
2.3 Reasoning method．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 8
2.4 Basic nodes operation．．．．．．．．．．．．．．．．．．．．．．．．．．．．．10
2.5 Supervised learning．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．13
2.6 Universal approximation．．．．．．．．．．．．．．．．．．．．．．．．．．．．15
3 Fine Tuning of Membership Functions 17
3.1 Introduction．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．17
3.2 Fine tuning of membership functions．．．．．．．．．．．．．．．．．．．．．．． 19
3.2.1 Gaussian function series．．．．．．．．．．．．．．．．．．．．．．．．．．19
3.2.2 Fine tuning method．．．．．．．．．．．．．．．．．．．．．．．．．．．．21
3.2.3 Tuning the FNN5．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 22
3.2.4 Convergence analysis．．．．．．．．．．．．．．．．．．．．．．．．．．． 23
3.2.5 Normalization of membership functions．．．．．．．．．．．．．．．．．．．． 24
3.3 Simulation results．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 26
4 Applications of the FNN systems
4.1 Tuning of PID controllers with specifications on gain and phase margins ......29
4.1.1 Introduction
4.1.2 Gain margin and phase margin
4.1.3 Tuning method using the FNN
4.1.4 Selection of training data and specification
4.1.5 Simulation results
4.2 Identification and Control of Hammerstein systems
4.2.1 Introduction
4.2.2 Hammerstein system
4.2.3 Identification model
4.2.4 Control design method
4.2.5 Convergence analysis
4.2.6 Simulation results
4.3 Fuzzy rules reduction
4.3.1 Introduction
4.3.2 Methods for reducing fuzzu rules
4.3.3 simulation result
2.6 Universal approximation．．．．．．．．．．．．．．．．．．．．．．．．．．．．15
5 Recurrent Fuzzy Neural Network 54
53.1 Introduction．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．54
5.2 Recurrent fuzzy neural networks: RFNN．．．．．．．．．．．．．．．．．．．．． 56
5.2.1 Structure of the RFNN．．．．．．．．．．．．．．．．．．．．．．．．．．．56
5.2.2 Layered operation ．．．．．．．．．．．．．．．．．．．．．．．．．．．．56
5.2.3 Fuzzy reasoning．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 59
5.3 Training architecture．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 61
5.3.1 Training architectures for identification and control．．．．．．．．．．．．．．． 61
5.3.2 Learning algorithm．．．．．．．．．．．．．．．．．．．．．．．．．．．．63
5.4 Stability analysis ．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 65
5.4.1 Stability analysis for identification．．．．．．．．．．．．．．．．．．．．．．66
5.4.2 Stability analysis for indirect control．．．．．．．．．．．．．．．．．．．．． 68
5.5 Simulation results．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 71
6 Conclusion and Future research
6.1 Conclusion
6.2 Future researches
42
Chung, I.-Fang, and 鐘翊方. "Reinforcement Neural Fuzzy Inference Networks and Its Applications." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/63962267050305328281.
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博士<br>國立交通大學<br>電機與控制工程系<br>88<br>In this thesis, aiming at the problem of reinforcement learning,
we propose the structure and associated learning algorithm of a neural fuzzy inference network for realizing the basic elements and functions of a traditional fuzzy logic controller. However, before we discuss the problem of reinforcement learning, we must construct a proper neural fuzzy inference network previously. Hence, at the beginning, we propose a basic five-layered connectionist network which could easily integrate the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure. If expert knowledge (or fuzzy rules) is provided from the outside world, here we could easily integrate expert knowledge into a network structure. Additionally, we derive the parameter learning method
according to the network structure and backpropagation learning scheme. The derived parameter learning method can be further used to adjust the network parameters for obtaining the best performance.
In addition, based upon the structure of the original five-layered connectionist network, we further propose a Fuzzy Adaptive Learning COntrol Network (FALCON). FALCON uses an on-line two-step learning algorithm, called FALCON-ART, for constructing the network structure dynamically. Compared with the original five-layered connectionist network, FALCON uses the fuzzy ART algorithm for structure learning in addition to the backpropagation learning scheme for parameter learning. The FALCON-ART can partition the input/output spaces on-line, tune membership functions and find proper fuzzy logic rules. All things are done automatically and dynamically. More notably, in this learning method, only the training data need to be provided from the outside world. That is, the users need not give the initial fuzzy partitions, membership functions and fuzzy logic rules. Hence, there is no input/output term nodes
and no rule nodes in the beginning of learning. The FALCON-ART partitions the pattern space into irregular hyperboxes and thus can avoid the problem of combinatorial growing of partitioned grids in some complex systems.
When the development of the structure and associated learning algorithm of a neural fuzzy inference network is finished, we extend the above network structure to the area of reinforcement learning: (1) We bring the genetic algorithms (GAs) into the structure learning of FALCON. GAs belong to a kind of search algorithms. Since GAs do not require or use derivative information, the most appropriate applications are problems
where gradient information is unavailable or costly to obtain.
Reinforcement learning is just one example of such a domain.
Therefore, we can regard GAs as a kind of reinforcement learning. In addition, due to its global optimization capability, GAs have become another useful tool to the automatic design of fuzzy control systems. Here an improved structure/parameter learning algorithm, called FALCON-GA, is proposed for constructing the FALCON automatically. The FALCON-GA is a three-phase hybrid learning algorithm. Except for using GAs to find proper fuzzy logic rules, its learning algorithms,
for partitioning input/output spaces and tuning membership functions, are the same as FALCON. By comparing the simulated results, we find that the performance of FALCON-GA is better than that of FALCON. (2) We propose a reinforcement Neuro-Fuzzy Combiner (NFC) for multiobjective control. Here the key component of NFC could be still FALCON. However, for solving the problems of complex multiobjective control and no instructive teaching information available, we add the concepts of the hierarchical control and the reinforcement learning into NFC structure. In more detail, the structure of the multiobjective control system is composed of the NFC and n existing low-level controllers. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the $n$ actions decided by the n low-level controllers and determine a proper action through reinforcement learning method to act on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once.
Capabilities and performances of the proposed reinforcement
neural fuzzy inference network have been verified and compared through various computer simulations. We have used FALCON-GA in solving the problems of the chaotic time-series prediction
and the control of the truck backer-upper. In the application of multiobjective control, we have also realized in a cart-pole balancing system and a crane system. Capabilities and performances of the proposed methods are all verified from these applications.
Abstract in English
Chapter 1: Introduction
Chapter 2: A Basic Five-layered Neural Fuzzy Inference Network
Chapter 3: A Fuzzy Adaptive Learning Control Network
Chapter 4: A GA-Based Fuzzy Adaptive Learning Control Network
Chapter 5: A Reinforcement Neuro-Fuzzy Combiner for
Multiobjective Control
Chapter 6: Conclusion
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Yen, Chung-fu, and 顏仲甫. "Defect Inspection Using Recurrent Fuzzy Cellular Neural Networks." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/49855127745617849325.
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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>95<br>The use of human vision for defect inspection from product images is limited to a certain quality level. In electronics industrial production lines, it is important to inspect the products for defects. It is feasible to check for product defects in the production lines by artificial means. Therefore, there is a need to develop methods using computer intelligence to replace manpower for product defect identification. We propose a framework to integrate a set of CNNs in parallel for solving defect identification as image processing problems. Our framework was modified from a generic recurrent fuzzy cellular neural network (RFCNN) that consists of a set of fuzzy IF–THEN rules. We employ a k-means algorithm for constructing the antecedent and consequent parts in the structure learning. To obtain the parameters of CNN templates, we derive a recurrent parameter learning algorithm based on ordered derivatives. We name our network modified RFCNN, mRFCNN. To validate the effectiveness of the proposed mRFCNN, we experiment different types of defects and compare our approach with a conventional defect inspection method, wherein CNN templates are trained by genetic algorithms (GACNN). The results of the experiments conclude that mRFCNN, compared to GACNN, has superior performance on more difficult image processing tasks.
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Lau, Chuang-Yeong, and 劉全勇. "An Automatic Melody Generation Using Fuzzy Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/68017460390309814549.
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碩士<br>國立中央大學<br>通訊工程研究所<br>100<br>The generated music from automatic music composition is not completely match the rule of music theory in the past research. This thesis proposed using fuzzy neural network (FNN) to training a repeating pattern melody which called refrain in pop music. A refrain usually repeats many times in the music objects. The proposed learning algorithm is based on fuzzy back propagation algorithm (FBP). The main goal of a fuzzy inference system is to model composer decision making within conceptual as the process of composing music. The music theory knowledge of consonance intervals and key signature were adopted to check and adjust the output melody to prevent incorrectly. The simulation results show that the proposed learning algorithm have a good learning ability and well performance.
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Lee, Hsin-Wei, and 李芯瑋. "MVC-Architecture Based Fuzzy-Neural-Networks Cloud-Computing." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/43793583221700537836.
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碩士<br>國立暨南國際大學<br>電機工程學系<br>102<br>Fuzzy Neural Network (FNN) is the most popular artificial intelligence research and is widely used in speech recognition, image processing, intelligent robotics, machine learning and data mining, etc. FNN combines the capability of fuzzy systems and artificial neural networks. The characteristic of fuzzy systems can mimic the vague information of the human brain and still make the right judgments. The most suitable FNN structure automatically adjusts after several iterations by the self-learning ability of artificial neural network.
FNN has self-learning ability and fuzzy inference advantages, many researchers use fuzzy neural network to solve classification problems. Thus, a service platform-FNN online which combines fuzzy neural network and cloud computing is presented in this thesis. FNN online provides instant online training, so that users can take advantage of fuzzy neural network to solve classification problems. The FNN online is built using the model (Model), View (View) and Controller (Controller), collectively known as the MVC design pattern. The concept of MVC is simple. Its development process in the software clearly defines the roles and high maintainability of the application. In the future works, we hope to join a variety of algorithms to allow the user to select the fitness one or management of membership function to provide a more complete service.
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Aghakhani, Sara. "Neuro-fuzzy architecture based on complex fuzzy logic." 2010. http://hdl.handle.net/10048/891.
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Thesis (M.Sc.)--University of Alberta, 2010.<br>Title from PDF file main screen (viewed on May 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering and Intelligent Systems, Department of Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.
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Lin, Jui-Wen, and 林瑞文. "The Prediction of Crude Oil Futures Prices - Comparison aming Backpropagation Neural Networks,Elman Recurrent Neural Networks and Recurrent Fuzzy Neural Networks." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/17140925737594266130.
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碩士<br>中原大學<br>企業管理研究所<br>94<br>During the past three years, oil price has changed dramatically and terrorists’ attacks caused the turbulent uneasiness of the global economy. Consequently, governments and corporate managers around the world actively sought effective methods to forecast the oil price more accurately than before for the purposes of hedging and arbitraging.
The purpose of this study is to predict the crude oil futures prices more accurately than traditional methods by using three popular non-parametric methods, namely, Backpropagation Neutral Networks (BPNs), Elman Recurrent Neural Networks (ERNNs), and Recurrent Fuzzy Neral Networks (RFNNs). This work also compares the learning and predictive performance among BPNs, ERNNs and RFNNs, and explores how training time impacts predictive accuracy.
The results show that the use of these three non-parametric methods to forecast the crude oil futures prices was appropriate since their values of MSE were all less than 0.0026767. Additionally, the learning ability was consistent by employing different training times. This investigation also indicates that the more training times the networks took, the better learning performance the networks have under most circumstances, the only exceptional case occurs at part two under FRNN model, where MSE is slightly less than that obtained from part three.
Regarding the predictive power of the three artificial neural networks (ANNs), this study finds that RFNNs has the best predictive power and BPN has the least predictive power among the three ANNs. This investigation also confirms that the predictive power can be enhanced by combining Fuzzy theory with the Recurrent Neural Network.
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Huang, Yu-Jie, and 黃煜傑. "Multivariate High-Order Weighted Fuzzy Time Series Based on Fuzzy Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/33520562389555656153.
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碩士<br>朝陽科技大學<br>資訊管理系碩士班<br>100<br>There are many uncertainty problems in the Human society, such as the forecasting of economic growth rate, financial crisis, etc. Since Song and Chissom proposed the concept of fuzzy time series in 1993, many scholars have proposed different models to deal with these problems. However, previous studies usually do not consider the factor selection and transfer original data to the fuzzy linguistic value by the subjective opinions in fuzzy process, which cannot objectively show the characteristics of the data. In addition, the fuzzy rules usually assign equal weight in the forecasting process, and it failed to consider the importance of each fuzzy rule. Based on above concepts, this study adopt the self-organizing map network (SOM) for the purpose of factor selection and proposed a multivariate high-order weighted fuzzy time series model based on fuzzy neural network (Fuzzy-BPN) and ordered weighted averaging operator (OWA) to make forecasts. In order to verify the proposed method, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from the Taiwan Stock Exchange Corporation are used in the experiment and filter the appropriate factors, and the experiment results are compared with other methods in with this study. The forecasting performance shows that the proposed method has better forecasting ability.
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"On the Synthesis of fuzzy neural systems." Chinese University of Hong Kong, 1995. http://library.cuhk.edu.hk/record=b5888338.
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by Chung, Fu Lai.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.<br>Includes bibliographical references (leaves 166-174).<br>ACKNOWLEDGEMENT --- p.iii<br>ABSTRACT --- p.iv<br>Chapter 1. --- Introduction --- p.1<br>Chapter 1.1 --- Integration of Fuzzy Systems and Neural Networks --- p.1<br>Chapter 1.2 --- Objectives of the Research --- p.7<br>Chapter 1.2.1 --- Fuzzification of Competitive Learning Algorithms --- p.7<br>Chapter 1.2.2 --- Capacity Analysis of FAM and FRNS Models --- p.8<br>Chapter 1.2.3 --- Structure and Parameter Identifications of FRNS --- p.9<br>Chapter 1.3 --- Outline of the Thesis --- p.9<br>Chapter 2. --- A Fuzzy System Primer --- p.11<br>Chapter 2.1 --- Basic Concepts of Fuzzy Sets --- p.11<br>Chapter 2.2 --- Fuzzy Set-Theoretic Operators --- p.15<br>Chapter 2.3 --- "Linguistic Variable, Fuzzy Rule and Fuzzy Inference" --- p.19<br>Chapter 2.4 --- Basic Structure of a Fuzzy System --- p.22<br>Chapter 2.4.1 --- Fuzzifier --- p.22<br>Chapter 2.4.2 --- Fuzzy Knowledge Base --- p.23<br>Chapter 2.4.3 --- Fuzzy Inference Engine --- p.24<br>Chapter 2.4.4 --- Defuzzifier --- p.28<br>Chapter 2.5 --- Concluding Remarks --- p.29<br>Chapter 3. --- Categories of Fuzzy Neural Systems --- p.30<br>Chapter 3.1 --- Introduction --- p.30<br>Chapter 3.2 --- Fuzzification of Neural Networks --- p.31<br>Chapter 3.2.1 --- Fuzzy Membership Driven Models --- p.32<br>Chapter 3.2.2 --- Fuzzy Operator Driven Models --- p.34<br>Chapter 3.2.3 --- Fuzzy Arithmetic Driven Models --- p.35<br>Chapter 3.3 --- Layered Network Implementation of Fuzzy Systems --- p.36<br>Chapter 3.3.1 --- Mamdani's Fuzzy Systems --- p.36<br>Chapter 3.3.2 --- Takagi and Sugeno's Fuzzy Systems --- p.37<br>Chapter 3.3.3 --- Fuzzy Relation Based Fuzzy Systems --- p.38<br>Chapter 3.4 --- Concluding Remarks --- p.40<br>Chapter 4. --- Fuzzification of Competitive Learning Networks --- p.42<br>Chapter 4.1 --- Introduction --- p.42<br>Chapter 4.2 --- Crisp Competitive Learning --- p.44<br>Chapter 4.2.1 --- Unsupervised Competitive Learning Algorithm --- p.46<br>Chapter 4.2.2 --- Learning Vector Quantization Algorithm --- p.48<br>Chapter 4.2.3 --- Frequency Sensitive Competitive Learning Algorithm --- p.50<br>Chapter 4.3 --- Fuzzy Competitive Learning --- p.50<br>Chapter 4.3.1 --- Unsupervised Fuzzy Competitive Learning Algorithm --- p.53<br>Chapter 4.3.2 --- Fuzzy Learning Vector Quantization Algorithm --- p.54<br>Chapter 4.3.3 --- Fuzzy Frequency Sensitive Competitive Learning Algorithm --- p.58<br>Chapter 4.4 --- Stability of Fuzzy Competitive Learning --- p.58<br>Chapter 4.5 --- Controlling the Fuzziness of Fuzzy Competitive Learning --- p.60<br>Chapter 4.6 --- Interpretations of Fuzzy Competitive Learning Networks --- p.61<br>Chapter 4.7 --- Simulation Results --- p.64<br>Chapter 4.7.1 --- Performance of Fuzzy Competitive Learning Algorithms --- p.64<br>Chapter 4.7.2 --- Performance of Monotonically Decreasing Fuzziness Control Scheme --- p.74<br>Chapter 4.7.3 --- Interpretation of Trained Networks --- p.76<br>Chapter 4.8 --- Concluding Remarks --- p.80<br>Chapter 5. --- Capacity Analysis of Fuzzy Associative Memories --- p.82<br>Chapter 5.1 --- Introduction --- p.82<br>Chapter 5.2 --- Fuzzy Associative Memories (FAMs) --- p.83<br>Chapter 5.3 --- Storing Multiple Rules in FAMs --- p.87<br>Chapter 5.4 --- A High Capacity Encoding Scheme for FAMs --- p.90<br>Chapter 5.5 --- Memory Capacity --- p.91<br>Chapter 5.6 --- Rule Modification --- p.93<br>Chapter 5.7 --- Inference Performance --- p.99<br>Chapter 5.8 --- Concluding Remarks --- p.104<br>Chapter 6. --- Capacity Analysis of Fuzzy Relational Neural Systems --- p.105<br>Chapter 6.1 --- Introduction --- p.105<br>Chapter 6.2 --- Fuzzy Relational Equations and Fuzzy Relational Neural Systems --- p.107<br>Chapter 6.3 --- Solving a System of Fuzzy Relational Equations --- p.109<br>Chapter 6.4 --- New Solvable Conditions --- p.112<br>Chapter 6.4.1 --- Max-t Fuzzy Relational Equations --- p.112<br>Chapter 6.4.2 --- Min-s Fuzzy Relational Equations --- p.117<br>Chapter 6.5 --- Approximate Resolution --- p.119<br>Chapter 6.6 --- System Capacity --- p.123<br>Chapter 6.7 --- Inference Performance --- p.125<br>Chapter 6.8 --- Concluding Remarks --- p.127<br>Chapter 7. --- Structure and Parameter Identifications of Fuzzy Relational Neural Systems --- p.129<br>Chapter 7.1 --- Introduction --- p.129<br>Chapter 7.2 --- Modelling Nonlinear Dynamic Systems by Fuzzy Relational Equations --- p.131<br>Chapter 7.3 --- A General FRNS Identification Algorithm --- p.138<br>Chapter 7.4 --- An Evolutionary Computation Approach to Structure and Parameter Identifications --- p.139<br>Chapter 7.4.1 --- Guided Evolutionary Simulated Annealing --- p.140<br>Chapter 7.4.2 --- An Evolutionary Identification (EVIDENT) Algorithm --- p.143<br>Chapter 7.5 --- Simulation Results --- p.146<br>Chapter 7.6 --- Concluding Remarks --- p.158<br>Chapter 8. --- Conclusions --- p.159<br>Chapter 8.1 --- Summary of Contributions --- p.160<br>Chapter 8.1.1 --- Fuzzy Competitive Learning --- p.160<br>Chapter 8.1.2 --- Capacity Analysis of FAM and FRNS --- p.160<br>Chapter 8.1.3 --- Numerical Identification of FRNS --- p.161<br>Chapter 8.2 --- Further Investigations --- p.162<br>Appendix A Publication List of the Candidate --- p.164<br>BIBLIOGRAPHY --- p.166
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Lin, Ming-yan, and 林明彥. "Implementation of Neural Fuzzy Networks with On-Chip Learning." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/kxc5dg.
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碩士<br>朝陽科技大學<br>資訊工程系碩士班<br>94<br>Field Programmable Gate Array (FPGA) has become an emerging hardware device recently. It makes the hardware designer able to perform task in a short period of time. The implementation of neural fuzzy network (NFN) is usually simulated by the software, but the speed is not fast enough to reach the demand for real time.
In this thesis, neural fuzzy network is implemented by the hardware. The hardware implementation of NFN with learning ability is very difficult. Although the backpropagation (BP) learning algorithm is widely used in the NFN, it is still too complicated to be implemented in the hardware. However, we use the simultaneous perturbation method as a learning scheme of the implementation hardware. It is different from BP that simultaneous perturbation method uses only values of the error function for modifying quantities to all parameters. In order to reduce area of a chip, we utilize the traditional non-linear activation function to replace the Gaussian function.
A major disadvantage of existing NFN is that their application is limited to static mapping problems as a result of their internal feedforward network structure. Inefficiency occurs for temporal problems. Hence, a recurrent neural fuzzy network (RNFN) capable of solving temporal problems is needed.
The hardware implementation of RNFN adds to random access memory (RAM) which stores all parameters of network into RAM, reduces the parameters spending on the logic area. The major findings of the experiment show that the NFN and RNFN retaining good performance.