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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lin, CTSubject: search.filters.subject.genetic algorithm

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    Controlling chaos by GA-based reinforcement learning neural network
    (1999-07-01)
    Lin, CT
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    Jou, CP
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    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the TDGAR learning system, Using the TD 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 uses the GA to adapt itself according to the internal reinforcement signal, This can usually accelerate the GA learning Since an external reinforcement signal may only be available at a time long after a sequence of actions have occurred in the reinforcement learning problems,By defining a simple external reinforcement signal, the TDGAR learning system can learn to produce a series of small perturbations to convert chaotic oscillations of a chaotic system into desired regular ones,vith a periodic behavior. The proposed method is an adaptive search for the optimum control technique. Computer simulations on controlling two chaotic systems, i.e., the Henon map and the logistic map, have been conducted to illustrate the performance of the proposed method.
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    New techniques on deformed image motion estimation and compensation
    (1999-12-01)
    Lin, CT
    ;
    Wu, GD
    ;
    Hsiao, SC
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa, Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages of individual and keep their merits at the same time, The proposed system can optimally balance the amount of data rate in tno aspects (i.e., compensation error and motion information), Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied for the moving object recognition in image sequence.
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    Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm
    (2001-06-01)
    Wang, CH
    ;
    Liu, HL
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    The stability analysis of the learning rate for a two-layer neural network (NN) Is discussed first by minimizing the total squared error between the actual and desired outputs for a set of training vectors. The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found fur this two-layer NN, It has also been proven in this paper that the dynamic stable learning rate for this two-layer NN must be greater than zero. Thus it is guaranteed that the maximum error reduction can be achieved by choosing the optimal learning rate for the next training iteration. A dynamic fuzzy neural network (FNN) that consists of the fuzzy linguistic process as the premise part and the two-layer NN as the consequence part is then illustrated as an immediate application of our approach, Each part of this dynamic FNN has its own learning rate for training purpose. A genetic algorithm is designed to allow a more efficient tuning process of the two learning rates of the FNN. The objective of the genetic algorithm is to reduce the searching time by searching for only one learning rate, which is the learning rate of the premise part, in the FNN. The dynamic optimal learning rates of the two-layer NN can he found directly using our innovative approach, Several examples are fully illustrated and excellent results are obtained for the model car backing up problem and the identification of nonlinear first order and second order systems.
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    Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
    (1998)
    Juang, CF
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. (1) The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SEFC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SEFC have been verified from this problem and from the comparisons with traditional GA-based fuzzy systems.