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Dissertations / Theses on the topic 'Fuzzy neural networks'

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Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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

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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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4

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

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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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6

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 descr

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7

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.

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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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9

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 con

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10

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

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11

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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12

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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13

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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14

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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15

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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16

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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17

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 associa

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18

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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19

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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20

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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21

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 estrutur

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22

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 t

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23

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 intr

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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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25

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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26

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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27

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

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28

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

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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 probl

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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 firs

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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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32

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 a

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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

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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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35

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

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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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37

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

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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>C

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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 diagn

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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 multil

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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 c

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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

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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 consonanc

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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 ab

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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 Neura

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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

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49

"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

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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 compl

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