標題: REINFORCEMENT STRUCTURE PARAMETER LEARNING FOR NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL-SYSTEMS
作者: LIN, CT
LEE, CSG
電控工程研究所
Institute of Electrical and Control Engineering
公開日期: 1-二月-1994
摘要: This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC's), each of which is a connectionist model with a feedforward multilayered, network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, both structure learning and parameter learning are performed simultaneously in the two NN-FLC's using the fuzzy similarity measure. The proposed RNN-FLCS can construct a fuzzy logic control and decision-making system automatically and dynamically through a reward/penalty signal (i.e., a ''good'' or ''bad'' signal) or through very simple fuzzy information feedback such as ''high,'' ''too high,'' ''low,'' and ''too low.'' The proposed RNN-FLCS is best applied to the learning environment, where obtaining exact training data is expensive. The proposed RNN-FLCS also preserves the advantages of the original NN-FLC, such as the ability to find proper network structure and parameters simultaneously and dynamically and to avoid the rule-matching time of the inference engine in the traditional fuzzy logic systems. Computer simulations were conducted to illustrate the performance and applicability of the proposed RNN-FLCS.
URI: http://dx.doi.org/10.1109/91.273126
http://hdl.handle.net/11536/2633
ISSN: 1063-6706
DOI: 10.1109/91.273126
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 2
Issue: 1
起始頁: 46
結束頁: 63
顯示於類別:期刊論文


文件中的檔案:

  1. A1994PT98800012.pdf