✦ LIBER ✦

An adaptive unidirectional linear response fuzzy controller based on reinforcement learning

✍ Scribed by Zheng Tang; Masakazu Komori; Okihiko Ishizuka; Koichi Tanno

Publisher: John Wiley and Sons
Year: 1996
Tongue: English
Weight: 493 KB
Volume: 79
Category: Article
ISSN: 1042-0967
DOI: 10.1002/ecjc.4430790203

No coin nor oath required. For personal study only.

✦ Synopsis

This paper presents an adaptive fuzzy controller using unidirectional linear response (ULR) elements. The basic functions for a fuzzy controller including membership, minimum and defuzzification functions are realized by the ULR elements. Because the ULR element has diode-like characteristics, it can be implemented by a diodeannected mean-opinion score (MOS) transistor in current-mode implementations. The hardware implementation of the fuzzy controller using ULR elements should also be very simple and straightforward. The ULR fuzzy controller is applied to an inverted pendulum problem, and the effectiveness of the proposed ULR controller architechm and its learning capability based on reinforcement learning are demonstrated.

📜 SIMILAR VOLUMES

A reinforcement learning-based scheme fo

A reinforcement learning-based scheme for direct adaptive optimal control of linear stochastic systems

✍ Wee Chin Wong; Jay H. Lee 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 169 KB

A self-learning and tuning fuzzy logic c

A self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcements

✍ Hung-Yuan Chung; Chih-Kuan Chiang 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 237 KB 👁 2 views

This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and