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Dissertations / Theses on the topic 'Neural network controller'

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Published: 3 June 2025

Last updated: 19 July 2025

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1

Sagiroglu, Serkan. "Adaptive Neural Network Applications On Missile Controller Design." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611106/index.pdf.

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In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry<br>a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers

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2

Haynes, Barry P. "A neural network adaptive controller for non-linear systems." Thesis, University of Portsmouth, 1997. https://researchportal.port.ac.uk/portal/en/theses/a-neural-network-adaptive-controller-for-nonlinear-systems(19584462-246e-4de3-9e80-cda4923a38c1).html.

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3

Karakasoglu, Ahmet. "Neural network-based approaches to controller design for robot manipulators." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185612.

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This dissertation is concerned with the development of neural network-based methods to the control of robot manipulators and focusses on three different approaches for this purpose. In the first approach, an implementation of an intelligent adaptive control strategy in the execution of complex trajectory tracking tasks by using multilayer neural networks is demonstrated by exploiting the pattern classification capability of these nets. The network training is provided by a rule-based controller which is programmed to switch an appropriate adaptive control algorithm for each component type of m

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4

Chan, Leonard. "Implementation of CMAC as a neural network controller on mechanical systems /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17135.pdf.

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5

Gonzalez, Juan. "Spacecraft Formation Control| Adaptive PID-Extended Memory Recurrent Neural Network Controller." Thesis, California State University, Long Beach, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10978237.

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<p> In today’s space industry, satellite formation flying has become a cost-efficient alternative solution for science, on-orbit repair and military time-critical missions. While in orbit, the satellites are exposed to the space environment and unpredictable spacecraft on-board disturbances that negatively affect the attitude control system’s ability to reduce relative position and velocity error. Satellites utilizing a PID or adaptive controller are typically tune to reduce the error induced by space environment disturbances. However, in the case of an unforeseen spacecraft distur

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Lang, Michael. "A real-time implementation of a neural-network controller for industrial robotics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0008/NQ35217.pdf.

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7

Rose, Stephen Matthew. "Online training of a neural network controller by improved reinforcement back-propagation." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19177.

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8

Rathbone, Kevin. "Evolving visually guided neural network robot arm controllers for lifetime learning." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327646.

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Ariss, Joseph, and Salim Rabat. "A comparison between a traditional PID controller and an Artificial Neural Network controller in manipulating a robotic arm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259365.

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Robotic and control industry implements different control technique to control the movement and the position of a robotic arm. PID controllers are the most used controllers in the robotics and control industry because of its simplicity and easy implementation. However, PIDs’ performance suffers under noisy environments. In this research, a controller based on Artificial Neural Networks (ANN) called the model reference controller is examined to replace traditional PID controllers to control the position of a robotic arm in a noisy environment. Simulations and implementations of both controllers

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Conlon, Martin J. "Design and evaluation of a neural network-based controller for an artificial heart." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57723.pdf.

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11

Conlon, Martin J. (Martin John) Carleton University Dissertation Engineering Mechanical and Aerospace. "Design and evaluation of a neural network-based controller for an artificial heart." Ottawa, 2000.

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12

Youmans, Elisabeth A. "Neural network control of space vehicle orbit transfer, intercept, and rendezvous maneuvers." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162101/.

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13

Kenevissi, Farhad. "Vertical motion control of twin-hull vessels in regular head seas using a neural optimal controller." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323487.

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14

Tang, Zhi-Dao, and 唐智德. "Neural Network Based Controller." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/52644385049229255267.

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15

Chen, Te-Yu, and 陳德育. "Robust Neural Network Controller Design." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/55863623086467862849.

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碩士<br>元智大學<br>電機工程學系<br>90<br>Neural networks with their massive parallelism and their learning capabilities offer the promise of better performance. This thesis focuses on the design of robust neural network controller which combines the neural network control and H∞ control design techniques. For the neural network control design, a recurrent neural network approximator is proposed, and an on-line parameter tuning methodology, using the gradient descent method and the Lyapunov stability theorem, is developed to increase the learning capability and to guarantee the systems’ stability. For H∞

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16

Li, Zhao Ji, and 李昭冀. "Neural fuzzy controller design with two-level neural network." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/58558698163485044944.

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Lin, Min-Chin, and 林旻致. "Parameterizable CMAC Neural Network Controller IP Generator." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/91052983643269859501.

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18

Lin, Fang-Wei, and 林芳蔚. "Design of PID Controller Using Neural Network." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/24941902148340316074.

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碩士<br>大葉大學<br>電機工程學系碩士班<br>94<br>Due to the robustness of PID (Proportional – Integral – Derivative) controller on control systems, this type of controllers are widely used in industrial plant design for years. There are many design methods and parameters adjustments for tuning PID parameters, one of the typical tuning methods is the Ziegler – Nichols (ZN) tuning one. However, large overshoot, long settling time are poor phenomena found in the step response after ZN tuning. More fine tunings are necessary to improve these time characteristics. Soft computings such as fuzzy set, artificial neur

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19

XU, SHUN-TANG, and 許順鏜. "Fuzzy logic controller as a neural network." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/60162101651486012092.

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Xu, Shun-Tang, and 許順鏜. "Fuzzy logic controller as a neural network." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/76502445639528045442.

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21

Yang, Neng-Jie, and 楊能傑. "An Optimal Recurrent Fuzzy Neural Network Controller." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/22893053061456487124.

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碩士<br>中原大學<br>電機工程研究所<br>90<br>In this thesis, an optimal recurrent fuzzy neural network controller is by an adaptive genetic algorithm. The recurrent fuzzy neural network has recurrent connections representing memory elements and uses a generalized dynamic backpropagation algoruthm to adjust fuzzy parameters on-line. Usually, the learning rate and the initial parameter values are chosen randomly or by experience, therefore is human resources consuming and inefficient. An adaptive genetic algorithm is used instead to optimize them. The adaptive genetic algorithm adjust the probability of cross

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Chiu, Yi-Feng, and 邱一峰. "STUDY ON SELF-CONSTRUCTING FUZZY NEURAL NETWORK CONTROLLER USING RECURRENT NEURAL NETWORK LEARNING STRATEGY." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/38808034711756082416.

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碩士<br>大同大學<br>電機工程學系(所)<br>101<br>In this thesis, the self-constructing fuzzy neural network controller (SCFNN) using recurrent neural network (RNN) learning strategy is proposed. For back-propagation (BP) algorithm of the SCFNN controller, the exact calculation of the Jacobian of the system cannot be determined. In this thesis, the RNN learning strategy is proposed to replace the error term of SCFNN controller. After the training of the RNN learning strategy, that will receive the relation between controlling signal and result of the nonlinear of the plant completely. Moreover, the structure

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23

Chen, Hsin-Li, and 陳信利. "Water Treatment Controller Design Using Neural Network Technique." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76106411538043373088.

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碩士<br>逢甲大學<br>自動控制工程所<br>93<br>In the operation of water treatment, the relationship between the coagulant dosage and the water quality parameters is non-linear in historical record data, and the affecting factors are quite complex. The normal dosage of coagulation depends on the jar tests and then adjusted to field actual dosage by operator experience in most of treatment plants. The conventional operation method can hardly in time to adjust to the proper dosage. PID controller has been used widely in the industry. In this paper, we adopt a wavelet neural network self-tuning PID controller, w

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Chang, Chi-Ming, and 張啟民. "Design of Servomotor Speed Controller Using Neural Network." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/45794043461427563259.

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碩士<br>逢甲大學<br>電機工程學系<br>88<br>Using neural network model instead of a traditional PI controller to control servomotor speed is proposed in this thesis. Firstly, a back propagation neural network is trained by sampling data, and its important parameters are optimized. There are three parts: training program, testing program and system program in our training network. From training program, the characteristic of back propagation neural network makes every neural node strength and weight value of processing unit bias to learn and adjust itself according to an error adjusting value. An error conve

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Chang, Wen-Bin, and 張文賓. "Neural-Network-Based Linear Combination Fuzzy Logic Controller." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/37314354910006924949.

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碩士<br>國立臺灣科技大學<br>工程技術研究所<br>81<br>To design a fuzzy logic controller without depending on control engineering and expert's experience, we implemented Sugeno's fuzzy logic control rule on multi- layer feedforward neural network. This neural network can be trained by backpropa- gation learning algorithm, so the parameters of fuzzy logic controller such as parameters of membership function and para- meters of consequence part of rules are designed by learning. The advan

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王聰文. "Controller design applying neural network on linear motor." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/86079437615870918302.

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27

Lin, Huan-Yu, and 林桓宇. "Realizing Robot Control by CMAC Neural Network Controller." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/62751246758751576893.

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碩士<br>國立交通大學<br>電機與控制工程系<br>88<br>The object of this paper is to study the behaviors of CMAC control system and to analyze the stability of the system, and then to compare with PID controller. To find out the characteristic between output and input, the Runge-Kutta method is used. The CMAC controller is proposed to emulate the pd-plus-gravity control for robotics. However, the CMAC requires no information about the robot, and can deal with large variations in load. The CMAC produces enormous integration action when the input vector moves slowly in the space, but it can also forget efficiently

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28

"Differentiable Harvard Machine Architecture with Neural Network Controller." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57204.

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abstract: There have been multiple attempts of coupling neural networks with external memory components for sequence learning problems. Such architectures have demonstrated success in algorithmic, sequence transduction, question-answering and reinforcement learning tasks. Most notable of these attempts is the Neural Turing Machine (NTM), which is an implementation of the Turing Machine with a neural network controller that interacts with a continuous memory. Although the architecture is Turing complete and hence, universally computational, it has seen limited success with complex real-world ta

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Lee, Ying Li, and 李英立. "The Architecture of Reconfigurable Critic Neural Network Controller." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/76770944548264741148.

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碩士<br>國立中正大學<br>電機工程研究所<br>90<br>The objective of this thesis is to implement the architecture of Reconfigurable Critic Neural Network Controller in an IC chip. This architecture is principally designed based on the CMAC with a computation power of a 32-bit CPU. The architecture includes two parts: one is a decoder which handles the process of external environment signals exciting the physical addresses; the other is a neural network controller which implements the CMAC scheme by firmware. The function of the neural network controller can accommodate a variety of decoding methods and learning

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30

Wang, Chung-Hao, and 王仲豪. "STUDY ON SELF-CONSTRUCTING FUZZY NEURAL NETWORK CONTROLLER USING RECURRENT WAVELET NEURAL NETWORK LEARNING STRATEGY." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/66373384738532600320.

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碩士<br>大同大學<br>電機工程學系(所)<br>102<br>In this thesis, the self-constructing fuzzy neural network controller (SCFNN) using recurrent wavelet neural network (RWNN) learning strategy is proposed. SCFNN has been proven over the years to simulate the relationship between input and output of the nonlinear dynamic system. Nevertheless, there are still has the drawback of training retard in this control method. The RWNN approach with a widely similar range of nature since the formation of wavelet transform through the dilation and translation of mother wavelet, it has capability to resolve time domain and

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Tsai, Meng-Jan, and 蔡孟展. "Neural Network Controller Design and Analysis for Induction Motor." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/31513404827134422093.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>93<br>Abstract The purpose of this thesis is to design and investigate the application of the self-tuning PI controller under indirect rotor-flux-oriented induction motor driver. In this paper, the controller use neural network concepts to provide speed control of induction motor that is robust to both the dynamic changes in plant parameters and the introduction of load disturbances. This neural netwrok is two layer and uesd RLS(Recurisive Least-Squares) Algorithm to minimize the difference between the motor’s actural response and that predicted by the neural estima

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Lee, Zheng-Hao, and 李正皓. "Adaptive Backstepping Neural Network Controller Design for Nonlinear Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/86720422859150258086.

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碩士<br>國立臺灣師範大學<br>工業教育學系<br>97<br>Three control methods for nonlinear systems are proposed in this thesis. The first controller design is about a B-spline adaptive backstepping controller for affine nonlinear systems. The controller is comprised of a B-spline neural network identifier and a robust controller. The B-spline neural network identifier is the main controller and the robust controller is developed to achieve tracking performance to a desired attenuation level. B-spline neural networks have the advantage over other neural networks of local output adjustment, allowing them to more e

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Huang, Zhong Qi, and 黃仲麒. "A neural network controller for twin-spool turbofan engine." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/57833341920625243912.

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Chen, Wen-Ming, and 陳文明. "A Neural-Network-Based Fuzzy Controller for Motion Control." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/97735866295678474174.

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35

ZHANG, YUAN-XUN, and 張元勳. "Two-level neural network system for learning controller design." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/71776171121031199350.

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36

甘能捷. "Design of Hopfield Neural Network Controller with Its applications." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/81746670928610668381.

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37

Chen, Pei-hen, and 陳沛亨. "Design Of Neural Network Based PID Gain Scheduling Controller." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/96534719659182560646.

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碩士<br>義守大學<br>電機工程學系碩士班<br>95<br>This research is based on the applications of neural network, such as artificial intelligence ,etc., neural networks can perform adaptive control through learning processes. But there are some problems, which should be solved in practice. The main problems are the slow learning speed, and uncertain pattern. Conventional PID controllers have many advantages so that they have been used widely as a major control method in industrial applications. However, the P, I, and D parameters are difficult to chose during the controller development, and can only be carried o

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Ho, Jhy-Lan, and 何智南. "Realization of CMAC Neural Network Controller by FPGA Chips." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/08478201583486504419.

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碩士<br>國立交通大學<br>控制工程系<br>85<br>The Cerebellar Model Articulation Controller(CMAC) is capable of learning nonlinear functions extremely quickly due to its generalizing capability,so itis a powerful and practical tool for real time control.In this studywe present a realization of the CMAC neural network by FPGA chips.We employ the fixed point system and adopt the SIMD architecture to implement the CMAC parallel algorithm.Hardware design is accomplished by three Xilinx XC4000 FPGA chips.Two

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39

YEH, TZU-CHUAN, and 葉紫泉. "Radial basis function neural-network controller for robotic manipulators." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c2vpud.

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碩士<br>華夏科技大學<br>智慧型機器人研究所<br>107<br>The difficulty of the control of multiple-input multiple-output (MIMO) systems is to eliminate the coupling effects between the degrees (DOFs) of free for MIMO systems. A robotic manipulator is one of MIMO systems, which possesses complicated and nonlinear dynamics characteristics. Therefore, it is difficult to design model-based to control robotic manipulators. This study developed a model-free radial basis function neural-network controller (RBFNC), which has characteristics of the coupling weighting, for the control of robotic manipulators. Simulation res

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Hsu, Ding-Teng, and 許定騰. "FUZZY NEURAL NETWORK-PID CONTROLLER APPLICATION FOR GANTRY SYSTEM." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/e3wf3x.

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碩士<br>大同大學<br>電機工程學系(所)<br>107<br>In this thesis, a controller combining PID and fuzzy neural network is designed. This controller is controlled by the PID controller as the main controller to control the gantry system. The fuzzy neural network is used to adjust the PID parameters, so that the PID controller has better control effect. The PID controller parameters affect the control effect of the PID controller, and it takes time and experience to find a proper set of PID parameters, so this thesis uses fuzzy neural network to find the appropriate PID parameters, learn by each input error and

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林玟叡. "Neural Network Based Optimal Fuzzy Controller Design for Nonlinear Systems." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/27089656634052171071.

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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>In this work, we propose an integrated approach to fuzzy modeling and optimal fuzzy control for unknown nonlinear systems. We first obtain the Takagi-Sugeno (T-S) fuzzy model of the nonlinear plant by linear self-constructing neural fuzzy inference network (linear SONFIN). With training input and output data of the nonlinear system, linear SONFIN can dynamically increase the number of fuzzy rules, and also adjust the parameters of each rule to minimize the output error. Then, if each fuzzy subsystems is completely controllable and completely observable, we can

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Hsu, Kuo-han, and 徐國翰. "Realization of CMAC Neural Network Controller by PC-based Design." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/66579237086028875348.

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碩士<br>國立交通大學<br>電機與控制工程學系<br>86<br>The object of this paper is to design a CMAC control system by PC-based design combined by traditional PID controller. All control signal will be generated by PC. Due to the speed rising of PC recently﹐The application of PC is more important in several field. The large counting time of Neural Network can be reduced. Therefore, PC-based control system is a good choose.We use the correcting CMAC learning rule to simulate a CMAC control system, in order to re

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43

Dahunsi, Olurotimi Akintunde. "Neural network-based controller designs for active vehicle suspension systems." Thesis, 2014.

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Vehicle suspension design necessitates achieving complex compromise between various performance objectives. Active vehicle suspension systems (AVSS) outperforms all the other suspension types in this regard but at the cost of higher bandwidth and power consumption as well as, physical space constraint. This limitations have however not hindered research on AVSS as some of the automobile manufacturers have started introducing AVSS in their products thereby prompting improvement of its current level of performance. The challenges of AVSS design centres around the inherent nonlinearities an

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Luo, Yi-wei, and 羅翊瑋. "Study of Recurrent Fuzzy Neural Network Controller for Ultrasonic Motor." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/25658787003659675392.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>96<br>The traveling-wave ultrasonic motor (TWUSM) is a new type of actuator with significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, compactness in size, no electromagnetic interference, silent drive, reversible controllability, and ability to maintain angle without electric power. Therefore, the TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone pro

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Cheng, Ming-hua, and 鄭明樺. "HYBRID PID-LIKE SELF-CONSTRUCTING FUZZY NEURAL NETWORK CONTROLLER DESIGN." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/36915802257157381022.

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碩士<br>大同大學<br>電機工程學系(所)<br>98<br>A hybrid proportional-integral-derivative fuzzy neural network (FNNPID) controller is designed for uncertain nonlinear systems in this thesis. The FNNPID controller includes three components which are a PID controller, an FNN estimator, and a robust controller. First, the PID controller is the main controller which uses the error, integral of the error, and derivation of the error with the corresponding parameters to control uncertain nonlinear systems. Next, the FNN estimator is used to estimate the parameters of the PID controller. The structure learning util

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Liang, Yi-hung, and 梁亦閎. "The Design of a Reconfigurable Reinforcement Learning Neural Network Controller." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/79559525647475717487.

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碩士<br>國立中正大學<br>電機工程研究所<br>91<br>The objective of this thesis is to implement the architecture of reconfigurable reinforcement learning neural network controller system. In order to bring the function of reconfigurable computing into full play, the architecture is mainly described with two parts. Inside the controller, there are a decoder which deals with process of external environment signals exciting physical addresses and a neural network controller which implements the algorithm by firmware. Outside the controller, the system is composed of a controller module, an EEPROM module and an SRA

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Huang, Kai-yu, and 黃凱昱. "Self-Tuning PID Neural Network Controller Design and its Application." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/52942607901574629525.

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碩士<br>國立聯合大學<br>電機工程學系碩士班<br>95<br>In this thesis, a self-tuning PID controller based on backpropagation neural network and recurrent neural network is presented. The proposed methods are applied to control the speed and position of DC motors to demonstrate their effectiveness. The backpropagation neural network belongs to the class of forward neural network. Its learning rule is to minimalize the energy function through adjusting weights with gradient descent method. For recurrent neural network, it combines the structure of forward and feedback neural network. The learning rule of recurrent

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Wu, Zong-Xiu, and 吳宗修. "Application of Neural Network Adaptive Controller in Time-Varying System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r3djyq.

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碩士<br>龍華科技大學<br>機械工程系碩士班<br>107<br>In traditional adaptive control, the gradient descent method or least square method is used to estimate the parameters of a time-varying system. The singularity problem in numerical analysis is easily caused due to the errors of numerical rounding and external environmental disturbances in computer calculation, which will affect the response and stability of the closed-loop control system. In this paper, a neural network adaptive controller is proposed. The parameters of a time-varying system are estimated online by using the neural network controller. The co

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Peng, Li-Hsien, and 彭立賢. "Application of the Neural Network to CNC Controller Parameters Optimization." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5311062%22.&searchmode=basic.

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碩士<br>國立中興大學<br>機械工程學系所<br>107<br>CNC machine tools play an important role in the mechanical industry. When the CNC machine tool is used for machining, there are three kinds of the processing indexes such as speed, accuracy and surface quality. Due to the Industry 4.0. the products are gradually oriented towards the trend of low volumes and high product variety. After the machine tool has been shipped from the factory, it will be set a set of standard controller parameter. But this set of controller parameter can’t conform all kinds of processing requirement. Therefore, it is extremely importa

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Chen, Jin-Liang, and 陳金良. "Study of the Fuzzy Neural Network Controller for Induction Motor Driver." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/2ybbr7.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>90<br>Fuzzy theorems have been proposed for several decades and used in various control systems. Fuzzy controllers avoid certain control system models and have strong tolerance for uncertain systems. However, the fuzzy rule base and membership functions are not easy to establish. It is difficult to design a traditional fuzzy controller for a system with uncertainty and parameter variations that can produce excellent response. 　　This thesis proposes a fuzzy neural network controller that combines the fuzzy theorem and neural network. Because the neural network has

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