Dissertations / Theses: 'Fuzzy systems; Neural networks'

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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.

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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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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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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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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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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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Ismael, Ali. "Neural adaptive control systems /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901244.

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Watanabe, Yukio. "Learning control of automotive active suspension systems." Thesis, Cranfield University, 1997. http://dspace.lib.cranfield.ac.uk/handle/1826/13865.

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This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.

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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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Turner, Kevin Michael. "Estimation of Ocean Water Chlorophyll-A Concentration Using Fuzzy C-Means Clustering and Artificial Neural Networks." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/TurnerKM2007.pdf.

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Chan, Wing-chi. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22925843.

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Geng, Guang. "Modelling and control of some nonlinear processes in air-handling systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386699.

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Bossley, Kevin Martin. "Neurofuzzy modelling approaches in system identification." Thesis, University of Southampton, 1997. https://eprints.soton.ac.uk/250027/.

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System identification is the task of constructing representative models of processes and has become an invaluable tool in many different areas of science and engineering. Due to the inherent complexity of many real world systems the application of traditional techniques is limited. In such instances more sophisticated (so called intelligent) modelling approaches are required. Neurofuzzy modelling is one such technique, which by integrating the attributes of fuzzy systems and neural networks is ideally suited to system identification. This attractive paradigm combines the well established learning techniques of a particular form of neural network i.e. generalised linear models with the transparent knowledge representation of fuzzy systems, thus producing models which possess the ability to learn from real world observations and whose behaviour can be described naturally as a series of linguistic humanly understandable rules. Unfortunately, the application of these systems is limited to low dimensional problems for which good quality expert knowledge and data are available. The work described in this thesis addresses this fundamental problem with neurofuzzy modelling, as a result algorithms which are less sensitive to the quality of the a priori knowledge and empirical data are developed. The true modelling capabilities of any strategy is heavily reliant on the model's structure, and hence an important (arguably the most important) task is structure identification. Also, due to the curse of dimensionality, in high dimensional problems the size of conventional neurofuzzy models gets prohibitively large. These issues are tackled by the development of automatic neurofuzzy model identification algorithms, which exploit the available expert knowledge and empirical data. To alleviate problems associated with the curse of dimensionality, aid model generalisation and enhance model transparency, parsimonious models are identified. This is achieved by the application of additive and multiplicative neurofuzzy models which exploit structural redundancies found in conventional systems. The developed construction algorithms successfully identify parsimonious models, but as a result of noisy and poorly distributed empirical data, these models can still generalise inadequately. This problem is addressed by the application of Bayesian inferencing techniques; a form of regularisation. Smooth model outputs are assumed and superfluous model parameters are controlled, sufficiently aiding model generalisation and transparency, and data interpolation and extrapolation. By exploiting the structural decomposition of the identified neurofuzzy models, an efficient local method of regularisation is developed. All the methods introduced in this thesis are illustrated on many different examples, including simulated time series, complex functional equations, and multi-dimensional dynamical systems. For many of these problems conventional neurofuzzy modelling is unsuitable, and the developed techniques have extended the range of problems to which neurofuzzy modelling can successfully be applied.

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Feng, Ming. "Local modelling and control of nonlinear systems." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326788.

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Coyne, Mark R. "Handling uncertainty in knowledge based systems using the theory of mass assignments." Thesis, University of Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357899.

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Gottschling, Andreas Peter. "Three essays in neural networks and financial prediction /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9728773.

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Lau, Chun Yin. "Extended adapative [i.e. adaptive] neuro-fuzzy inference systems." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070130.170625/index.html.

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陳穎志 and Wing-chi Chan. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241475.

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Wang, Bo-Hyeun. "Fuzzy associative memories identification and control of complex systems." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13418.

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Vasilic, Slavko. "Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/436.

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This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section.

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Liu, Ying Kin. "Load-distributing algorithm using fuzzy neural network and fault-tolerant framework /." access abstract and table of contents access full-text, 2006. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-ee-b21471423a.pdf.

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Thesis (M.Phil.)--City University of Hong Kong, 2006.
"Submitted to Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 88-92)

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Juang, Jih-Gau. "Robotic gait synthesis and control design using neural and fuzzy networks approaches /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9924894.

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Che, Fidelis Ndeh. "Object-oriented analysis and design of computational intelligence systems." Thesis, City University London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245861.

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Weeraprajak, Issarest. "Faster Adaptive Network Based Fuzzy Inference System." Thesis, University of Canterbury. Mathematics and Statistics, 2007. http://hdl.handle.net/10092/1234.

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It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the partial derivative on the weight step in the back-propagation-type gradient descent algorithm. *In some cases the matrices in the least square estimator can be ill-conditioned. *Several estimators are known which dominate, or outperform, the least square estimator. Therefore this thesis develops a new system that overcomes the above problems, which is called the "Faster Adaptive Network Fuzzy Inference System" (FANFIS). The new system in this thesis is shown to significantly out perform the existing method in predicting a chaotic time series , modelling a three-input nonlinear function and identifying dynamical systems. We also use FANFIS to predict five major stock closing prices in New Zealand namely Air New Zealand "A" Ltd., Brierley Investments Ltd., Carter Holt Harvey Ltd., Lion Nathan Ltd. and Telecom Corporation of New Zealand Ltd. The result shows that the new system out performed other competing models and by using simple trading strategy, profitable forecasting is possible.

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Hudgins, Billy E. "Implementation of fuzzy inference systems using neural network techniques." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23919.

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Van, Den Bosch Magali Marie. "Simulation of ion exchange processes using neuro-fuzzy reasoning." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/2161.

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Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.
Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.

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Wu, Kwok-Chiu, and 胡國釗. "Development of electric vehicle battery capacity estimation using neuro-fuzzy systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B27777716.

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Mok, Hing-tung. "Online fault detection and isolation of nonlinear systems based on neurofuzzy networks." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39794064.

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Mok, Hing-tung, and 莫興東. "Online fault detection and isolation of nonlinear systems based on neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39794064.

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Daneshpooy, Alireza. "Artificial neural network and fuzzy logic control for HVDC systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23593.pdf.

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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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Reutzel, Edward W. "On the limitations and extensions of bidirectional associative memories in neural networks and fuzzy logic control theory." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16870.

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Chan, Yat-fei. "Neurofuzzy network based adaptive nonlinear PID controllers." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43958357.

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Chan, Yat-fei, and 陳一飛. "Neurofuzzy network based adaptive nonlinear PID controllers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43958357.

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Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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NETO, LUIZ SABINO RIBEIRO. "ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC AND NEURO-FUZZY SYSTEM IN THE ROLE OF SHORT TERM LOAD FORECAST." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7419@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A.
Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo sobre previsão de carga e sobre as variáveis que influenciam o comportamento da carga; um estudo da aplicação de técnicas de inteligência computacional em previsão de carga; a definição de sistemas de redes neurais, lógica fuzzy e neuro-fuzzy em previsão de carga; e estudo de casos. No estudo sobre previsão de carga, foi observada a influência de algumas variáveis no comportamento da curva de carga de uma empresa de energia elétrica. Entre estas variáveis se encontram alguns dados meteorológicos (Temperatura, Umidade, Luminosidade, Índice de conforto, etc.), além de informações sobre o perfil de consumo de carga das empresas. Também foi observado o comportamento da série de carga com relação ao dia da semana, sua sazonalidade e a correlação entre o valor atual e valores passados. Foi realizado um levantamento bibliográfico sobre a aplicação de técnicas de inteligência computacional na previsão de carga. Os modelos de redes neurais, são os mais explorados até o momento. Os modelos de lógica fuzzy começaram a ser utilizados mais recentemente. Modelos neuro-fuzzy são mais recentes que os demais, não existindo portanto, muita bibliografia a respeito. Os projetos de aplicação dos três modelos foram classificados quanto à sua arquitetura, desempenho, erros medidos, entradas utilizadas e horizonte da previsão. Foram propostos e implementados 4 sistemas de previsão de carga: lógica fuzzy, redes neurais, sistema neuro-fuzzy hierárquico e um sistema híbrido neural/neuro- fuzzy. Os sistemas foram especializados para cada dia da semana, pelo fato do comportamento da carga ser distinto entre estes dias. Para os sistemas neural, neuro-fuzzy e híbrido os dados também foram separados em inverno e verão, pois o perfil de consumo de carga é diferente nestas estações. O sistema com lógica fuzzy foi modelado para realizar previsões de curtíssimo prazo (10 em 10 minutos), utilizando para isto o histórico de carga, hora do dia e intervalo de dez minutos dentro da hora do dia. As regras do sistema foram geradas automaticamente a partir do histórico de carga e os conjuntos nebulosos foram pré-definidos. O sistema com redes neurais teve sua arquitetura definida através de experimentos, utilizando- se apenas dados de carga, hora do dia e mês como entradas. O modelo de rede escolhido foi com retropropagação do erro (backpropagation). Foram realizados testes incluindo outras entradas como temperatura e perfil de consumo. Para o sistema neuro-fuzzy foi escolhido um sistema neuro-fuzzy hierárquico, que define automaticamente sua estrutura e as regras a partir do histórico dos dados. Em uma última etapa, foi estudado um sistema híbrido neural/ neuro- fuzzy, no qual a previsão da rede neural é uma entrada do sistema neuro-fuzzy. Para os três últimos modelos as previsões realizadas foram em curto prazo, com um horizonte de uma hora Os sistemas propostos foram testados em estudos de casos e os resultados comparados entre si e com os resultados obtidos em outros projetos na área. Os dados de carga utilizados no sistema com lógica fuzzy foram da CEMIG, no período de 1994 a 1996, em intervalos de 10 minutos, para previsões em curtíssimo prazo. Os resultados obtidos podem ser considerados bons em comparação com um sistema de redes neurais utilizando os mesmos dados. Para os demais modelos foram utilizados os seguintes dados: dados horários de carga da Light e da CPFL, no períod
This thesis examines the performance of computational intelligence in short term load forecasting. The main objective of the work was to propose and evaluate neural network, fuzzy logic, neurofuzzy and hybrid systems in the role of short term load forecast, considering some variables that affect the load behavior such as temperature, comfort indexes and consumption profile. The work consisted in four main steps: a study about load forecasting; the modeling of neural network systems, fuzzy logic and neurofuzzy related to load forecast; and case studies. In the load forecasting studies, some variables appeared to affect the behavior of the load curve in the case of electrical utilities. These variables include meteorological data like temperature, humidity, lightening, comfort indexes etc, and also information about the consumption profile of the utilities. It was also noted the distinct behavior of the load series related to the day of the week, the seasonableness and the correlation between the past and present values. A bibliographic research concerning the application of computational intelligence techniques in load forecasting was made. This research showed that neural network models have been largely employed. The fuzzy logic models have just started to be used recently. Neuro-fuzzy are very recent, and there are almost no references on it. The surveyed application projects using the three models were classified by its architecture, performance, measured errors, inputs considered and horizon of the forecast. In this work four systems were proposed and implemented for load forecasting: fuzzy logic, neural network, hierarchical neuro-fuzzy and hybrid neural/neuro- fuzzy. The systems were specialized for each day of the week, due to the different behavior of the load found for each of the days. For the neural network, neuro-fuzzy and hybrid, the data were separated in winter and summer, due to the different behavior of the load in each of the seasons. The fuzzy logic system was modeled for very short term forecasting using the historic load for each hour of the day, in steps of 10 minutes within each hour. The fuzzy system rules were generated automatically based on the historic load and the fuzzy sets were pre-defined. The system with neural network had its architecture defined through experiments using only load data, hour of the day and month as input. The network model chosen was the back- propagation. Tests were performed adding other inputs such as temperature and consumption profile. For the neural- fuzzy, a hierarchical neuro-fuzzy system, which defines automatically its structure and rules based on the historical data, was employed. In a further step, a hybrid neural/neuro-fuzzy was studied, so as the neural network forecast is the input for the neuro-fuzzy system. For the last three models, short term forecasting was made for one hour period. The proposed systems were tested in case studies, and the results were compared themselves and with results obtained in other projects in the same area. The load data of CEMIG between 1994 and 1996 was used in the fuzzy logic system in steps of 10 minutes for very short term forecasting. The performance was good compared with a neural network system using the same data. For the other models, short term load forecasting (I hour, 24 steps ahead) was done using the following data: load data of LIGHT and CPFL between 1996 and 1998; temperature (hourly for LIGHT and daily for CPFL); the codification of month and hour of the day; and a profile of load by consumption class. For doing. The error results obtained by the models were around 1,15% for the fuzzy logic, 2,0% for the neural network, 1,5% for the neuro-fuzzy system, and 2,0% for the hybrid system. This work has showed the applicability of the computational intelligence techniques on load forecasting, demonstrating that a preliminary study of the series and their relation with

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Liut, Daniel Armando. "Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/29163.

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The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO).
Ph. D.

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Wanous, Mohammed. "A neurofuzzy expert system for competitive tendering in civil engineering." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343683.

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Mounce, Stephen Robert. "A hybrid neural network fuzzy rule-based system applied to leak detection in water pipeline distribution networks." Thesis, University of Bradford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695062.

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Ara?jo, J?nior Jos? Medeiros de. "Identifica??o n?o linear usando uma rede fuzzy wavelet neural network modificada." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15249.

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Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed
Nas ?ltimas d?cadas, as redes neurais t?m se estabelecido como uma das principais ferramentas para a identifica??o de sistemas n?o lineares. Entre os diversos tipos de redes utilizadas em identifica??o, uma que se pode destacar ? a rede neural wavelet (ou Wavelet Neural Network - WNN). Esta rede combina as caracter?sticas de multirresolu??o da teoria wavelet com a capacidade de aprendizado e generaliza??o das redes neurais, podendo fornecer modelos mais exatos do que os obtidos pelas redes tradicionais. Uma evolu??o das redes WNN consiste em combinar a estrutura neuro-fuzzyANFIS (Adaptive Network Based Fuzzy Inference System) com estas redes, gerando-se a estrutura Fuzzy Wavelet Neural Network - FWNN. Essa rede ? muito similar ?s redes ANFIS, com a diferen?a de que os tradicionais polin?mios presentes nos consequentes desta rede s?o substitu?dos por redes WNN. O presente trabalho prop?e uma rede FWNN modificada para a identifica??o de sistemas din?micos n?o lineares. Nessa estrutura, somente fun??es waveletss?o utilizadas nos consequentes. Desta forma, ? poss?vel obter uma simplifica??o da estrutura com rela??o a outras estruturas descritas na literatura, diminuindo o n?mero de par?metros ajust?veis da rede. Para avaliar o desempenho da rede FWNN com essa modifica??o, ? realizada uma an?lise das caracter?sticas da rede, verificando-se as vantagens, desvantagens e o custo-benef?cio quando comparada com outras estruturas FWNNs. As avalia??es s?o realizadas a partir da identifica??o de dois sistemas simulados tradicionalmente encontrados na literatura e um sistema real n?o linear, consistindo de um tanque de multisse??es e n?o linear. Por fim, a rede foi utilizada para inferir valores de temperatura e umidade no interior de uma incubadora neonatal. A execu??o dessa an?lise baseia-se em v?rios crit?rios, tais como: erro m?dio quadr?tico, n?mero de ?pocas de treinamento, n?mero de par?metros ajust?veis, vari?ncia do erro m?dio quadr?tico, entre outros. Os resultados encontrados evidenciam a capacidade de generaliza??o da estrutura modificada, apesar da simplifica??o realizada

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Ollé, Tamás. "Klasifikace vzorů pomocí fuzzy neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219728.

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Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

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Juma, Sarah Awuor. "A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval." Diss., Online access via UMI:, 2005.

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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.

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Souza, Aline Cristina de 1991. "Memórias associativas recorrentes exponenciais fuzzy baseadas em medidas de similaridade." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306025.

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Orientador: Marcos Eduardo Ribeiro do Valle Mesquita
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
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Resumo: Memórias associativas são modelos matemáticos inspirados pela capacidade do cérebro humano de armazenar e recordar informações por meio de associações. Tais modelos são projetados para armazenar um conjunto finito de associações chamado de conjunto das memórias fundamentais. Além disso, espera-se que a memória associativa seja capaz de recuperar uma informação armazenada mesmo a partir de um item incompleto ou ruidoso. As Memórias Associativas Recorrentes Exponenciais Fuzzy (REFAMs, acrônimo do termo em inglês Recurrent Exponential Fuzzy Associative Memories) podem ser efetivamente utilizadas para o armazenamento e recordação de uma família finita de conjuntos fuzzy. Em geral, uma REFAM define recursivamente uma sequência de conjuntos fuzzy obtidos usando médias ponderadas e exponenciais dos valores de medida de similaridade. Experimentos computacionais relacionados à recuperação de imagens em tons de cinza ruidosas mostraram que os novos modelos podem apresentar ótima capacidade absoluta de armazenamento bem como excelente tolerância a ruído
Abstract: Associative memories are mathematical models inspired by the human brain ability to store and recall information by means of associations. Such models are designed for the storage of a finite set of associations called the fundamental memories set. Furthermore, the associative memory is expected to be able to retrieve a stored information even from an incomplete or noisy item. The Recurrent Exponential Fuzzy Associative Memories (REFAMs) can be effectively used for storage and recall of a finite family of fuzzy sets. In general, a REFAM defines recursively a sequence of fuzzy sets obtained using weighted averages and exponentials of similarity measure values. Computational experiments concerning the retrieval of noisy gray-scale images revealed that the novel models may exhibit optimal absolute storage capacity as well as excellent noise tolerance
Mestrado
Matematica Aplicada
Mestra em Matemática Aplicada

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Sandmann, Humberto Rodrigo. "Predição não-linear de séries temporais usando sistemas de arquitetura neuro-fuzzy." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-01042009-095125/.

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Esta dissertação tem como objetivo a aplicação de sistemas com arquitetura neuro-fuzzy na predição de funções que geram séries temporais. A arquitetura pesquisada é a Adaptive Neuro-Fuzzy Inference System (ANFIS). Esta arquitetura se trata de um Fuzzy Inference Systems (FIS) im- plementado sob o paradigma das redes neurais artificiais. Ao fazer o uso da tecnologia de redes neurais artificiais, o ANFIS possui a capacidade de apren- dizagem dos dados do ambiente no qual está inserido. Da mesma forma, por implementar um FIS, o ANFIS agrega também a competência de processamento linguístico. Logo, o ANFIS pode ser categorizado como um sistema híbrido. Ao longo dos capítulos estão expostos alguns conceitos e fundamentos da Teoria Fuzzy, assim como das redes neurais artificiais e sistemas híbridos. Ao final do trabalho são realizadas algumas discussões, análises e conclusões, as quais permitem a possibilidade de futuras aplicações e extensão deste.
This master dissertation has as main objetive applies systems of neuro-fuzzy architecture for functions prediction in serie times. The architecture carried out is the Adaptive Neuro-Fuzzy Inference System (ANFIS). This architecture is a kind of Fuzzy Inference Systems (FIS) implemen- tation under a paradigm of arti¯cial neural networks. Making use of technology of arti¯cial neural networks, the ANFIS has the capacity of learning with environ- ment data that inserted on. As the same, the ANFIS had been implemented to be a FIS. Then it can process simbolic variables. So, an ANFIS can be described like a hibrid system. All over the chapters are showed some concepts and fundaments of Fuzzy theory, arti¯cial neural networks and hidrid systems. The purpose of the tests the ANFIS, it were been made from a logistic function and a Mackey-Glass function. This tests were against with an estimation function made by MLP net. At the end of the work are some discussions, analyses and conclusions that allows futures possibilites of applications and extensions of this work.

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Mossin, Eduardo André. "Diagnóstico automático de redes Profibus." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102012-162642/.

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Esta tese propõe a utilização de sistemas inteligentes para, automaticamente, realizar diagnósticos e localizar falhas na instalação e na operação de redes de comunicação industrial que utilizam o protocolo Profibus DP. Para tais tarefas, uma série de análises é realizada a partir dos sinais transmitidos pela camada física, de telegramas transmitidos pela camada de enlace e de funções da camada de usuário do protocolo Profibus DP. Para a análise da camada física, amostras dos sinais elétricos transmitidos são processadas e apresentadas a algumas Redes Neurais Artificiais para que sejam classificadas de acordo com a sua forma de onda. Caso estes sinais apresentem alguma deformação, o sistema indica uma provável causa para o problema, afinal, os problemas das redes Profibus originam padrões específicos e característicos impressos nas formas de onda do sinal digital. Ainda através da análise das amostras dos sinais oriundos da camada física, algumas fontes de problemas são detectadas a partir da análise do nível médio de tensão do sinal que um determinado dispositivo está transmitindo. Tal análise é realizada a partir de um Sistema Especialista. Também utilizando Sistemas Especialistas, os telegramas transmitidos pela camada de enlace deste protocolo são analisados e a partir destes, falhas de configuração são detectadas. Por fim, é proposto um sistema nebuloso responsável por indicar ao usuário um valor próximo ao ideal para a variável de tempo denominada target rotation time. A proposta foi testada e validada a partir de dados obtidos de redes Profibus estabelecidas em laboratório e de alguns dados sintéticos originados por software. Os resultados obtidos foram suficientes para a comprovação da tese de que sistemas computacionais inteligentes podem contribuir de maneira efetiva no diagnóstico de problemas em redes Profibus DP e até mesmo em outros tipos de rede.
This thesis proposes the use of intelligent systems to automatically perform diagnostics and locate faults during the installation and operation of industrial communication networks that use the Profibus DP protocol. For such tasks, some analyzes are performed from the signals transmitted by the physical layer, from telegrams transmitted by the data link layer and from some user layer functions of the Profibus DP protocol. For physical layer analysis, the transmitted electrical signals samples are processed and submitted for some artificial neural networks that classifies each signal according to its waveshape. If these signals have some deformation, the system indicates a probable cause for the problem, after all, the Profibus problems originate specific and characteristic patterns printed on the digital signal waveform. Still analyzing the physical layer signal samples, some problems sources are detected from the signal voltage analysis. Such analysis is performed from an Expert System. Also using expert systems, the data link layer telegrams are analyzed and configuration faults are detected. Finally, it is proposed a fuzzy system responsible for specify a value close to ideal for the target rotation time variable. The proposal has been tested and validated with data from Profibus networks established in laboratory. Besides, some synthetic data were generated by software. The results were sufficient to prove the thesis that intelligent computational systems can contribute effectively to diagnose problems in Profibus DP networks and even in other types of networks.

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Weerasinghe, Manori. "Fault detection and diagnosis for complex multivariable processes using neural networks." Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298141.

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Dissertations / Theses on the topic 'Fuzzy systems; Neural networks'

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