Browsing by Author "Jou, CP"

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Controlling chaos by GA-based reinforcement learning neural network
(1999-07-01)
Lin, CT
;
Jou, CP
;
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.
GA-based fuzzy reinforcement learning for control of a magnetic bearing system
(2000-04-01)
Lin, CT
;
Jou, CP
;
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) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task, The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system, The action network can be a normal neural network or a neural fuzzy network, 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, The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem, The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.
GA-based reinforcement learning for neural networks
(1998-03-01)
Lin, CT
;
Jou, CP
;
Lin, CJ
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A genetic reinforcement neural network (GRNN) is proposed to solve various reinforcement learning problems. The proposed GRNN is constructed by integrating two feedforward multilayer networks. One neural network acts as an action network for determining the outputs (actions) of the GRNN, and the other as a critic network to help the learning of the action network. 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 uses the genetic algorithm (GA) to adapt itself according to the internal reinforcement signal. The key concept of the proposed GRNN learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA. This learning scheme forms a novel hybrid GA, which consists of the temporal difference and gradient descent methods for the critic network learning, and the GA for the action network learning. By using the internal reinforcement signal as the fitness function, the GA can evaluate the candidate solutions (chromosomes) regularly, even during the period without external reinforcement feedback from the environment. Hence, the GA can proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning because a reinforcement signal. This can usually accelerate the GA learning because a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problems. Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme.