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Multipurpose virtual-reality-based motion simulator
(2000-01-01)
Lin, CT
;
Chung, IF
;
Liann, SA
;
Su, FY
;
電控工程研究所
Public security has become an important issue everywhere. Especially, the safe manipulation and control of various machines and vehicles has gained special attention such that the authorities keep emphasizing the strict training and censoring of human operators. Currently, such training and censoring process usually relies on the actual machines, equipment, or vehicles in the real sites. This not only has high demands in space, time and cost, but also causes another phase of public security problem. In this connection, the world-wide trend is to tackle the above dilemma by using virtual reality (VR). However, the current researches or products on Vn are more matured in the software display part of VR. How to combine 3D VR display with motion platform to achieve the aforementioned training and censoring purposes is an important research issue. This paper focuses on this research issue, and the god is to develop a multipurpose virtual-reality-based motion simulation system to meet the requirements of public security in training and censoring of human operators.
An adaptive neural fuzzy filter and its applications
(1997-08-01)
Lin, CT
;
Juang, CF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper, The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules, The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning), In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters, In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error, There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds, However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes, Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically. Moreover, in contrast to traditional fuzzy systems where the input-output spaces are partitioned as grid type causing the combinatorial growing of fuzzy rules as the input-output dimensions increase, irregular partitions are done in the ANFF according to the distribution of training data so fewer fuzzy rules will be generated, To demonstrate the performance of the ANFF, two applications, the nonlinear channel equalization and the adaptive noise cancellation, are simulated, Efficiency and advantages of the ANFF are verified by these simulations and comparisons.
Reinforcement learning for an ART-based fuzzy adaptive learning control network
(1996-05-01)
Lin, CJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON) for solving various reinforcement learning problems, The proposed RFALCON is constructed by integrating two fuzzy adaptive learning control networks (FALCON's), each of which is a connectionist model with a feedforward multilayer network developed for the realization of a fuzzy controller, One FALCON performs as a critic network (fuzzy predictor), and the other as an action network (fuzzy controller), Using the temporal difference prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network, The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal, An ART-based reinforcement structure/parameter-learning algorithm is developed for constructing the RFALCON dynamically, During the learning process, both structure learning and parameter learning are performed simultaneously in the two FALCON's, The proposed RFALCON can construct a fuzzy control system dynamically and automatically through a reward/penalty signal (i.e., a ''good'' or ''bad'' signal), It is best applied to the learning environment, where obtaining exact training data is expensive, The proposed RFALCON has two important features, First, it reduces the combinatorial demands placed by the standard methods for adaptive Linearization of a system, Second, the RFALCON is a highly autonomous system, Initially, there are no hidden nodes (i.e., no membership functions or Fuzzy rules), They are created and begin to grow as learning proceeds, The RFALCON can also dynamically partition the input-output spaces, tune activation (membership) functions, and find proper network connection types (fuzzy rules), Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme.
A second-order learning algorithm for multilayer networks based on block Hessian matrix
(1998-12-01)
Wang, YJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H-b, is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H-b, and verify the good approximation of H-b to H; H-b preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H-b recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made. (C) 1998 Elsevier Science Ltd. All rights reserved.
Application of fuzzy control to a road tunnel ventilation system
(1998-11-16)
Chen, PH
;
Lai, JH
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper deals with the serious problems of ventilation system in a large road tunnel. Higher visibility and lower concentration of carbon monoxide are the key issues concerning the ventilation system. Prior to designing the fuzzy control model, a configuration layout of the ventilation system including sensing, control and traffic prediction as well is conceptually constructed. Based on the layout that offers assignments of sensors and control elements, a fuzzy logic control model is developed. Membership functions of sensor errors and control increments are physically submitted in order to set up the fuzzy logic rules. Timing and spacing filtering in terms of weighting approaches is employed in the fuzzy logic rules. A dynamic equation describing the concentration of air pollution is also given so as to cooperate with the fuzzy logic rules and to play roles in the computer simulation. The result of computer simulation involving five cases indicates that a multi-level scheme is able to solve the engineering problems. (C) 1998 Elsevier Science B.V. All rights reserved.
An ART-based fuzzy adaptive learning control network
(1997-11-01)
Lin, CJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm called FALCON-ART is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The FALCON-ART has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into ''grids.'' As the number of input/output variables increases, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the FALCON-ART partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the FALCON-ART can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or even any initial information on these. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online incoming training data. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system.
Reinforcement learning for an ART-based fuzzy adaptive learning control network (vol 7, pg 709, 1996)
(1996-09-01)
Lia, CJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Adaptive subsethood for neural fuzzy control
(1996-10-01)
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The paper extends Kosko's fuzzy measure of subsethood (Kosko 1992) to a measure of mutual subsethood or fuzzy equivalence. Gaussian or bell-shaped fuzzy sets then simplify the new measure and allow supervised learning to learn and tune the fuzzy rules. The gaussian sets act as nodes in neural fuzzy control networks and give a simple closed form for the measure of mutual subsethood. The new adaptive subsethood controller (ASC) system uses the network structure to store, learn, and tune fuzzy rules. Simulations show how the ASC system can control a model car, balance an inverted wedge, and control the ball and beam system.
A neural fuzzy system with fuzzy supervised learning
(1996-10-01)
Lin, CT
;
Lu, YC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper, This system is able to process and learn numerical information as well as linguistic information, At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system, The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference, We use a-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape, Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons, Based on interval arithmetics, a fuzzy supervised learning algorithm; is developed for the proposed system, It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available, The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values, With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base, Simulation results are presented to illustrate the performance and applicability of the proposed system.
Speaker adaptation of fuzzy-perceptron-based speech recognition
(1999-02-01)
Lin, CT
;
Nein, HW
;
Lin, WF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplanes need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.

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