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-Feb-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 |
Appears in Collections: | Articles |
Files in This Item:
If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.