1997 Volume 63 Issue 608 Pages 1261-1268
Recently, fuzzy systems have been applied to various systems. However, due to lack of learning ability, the determination of most fuzzy rules and membership functions was made by human experts. In this paper, we propose a self-tuning fuzzy controller with a virus-evolutionary genetic algorithm (VEGA). This learning algorithm is based on the virus theory of evolution. VEGA realizes horizontal propagation and vertical inheritance of genetic information in a population. The main operator of VEGA is a reverse transcription operator which plays the roles of crossover and selection simultaneously. Furthermore, a transduction operator generates a substring to be transmitted. VEGA can reduce the number of fuzzy rules using the reverse transcription operator and the transduction operator. The effectiveness of the proposed method is demonstrated through the simulation of a cart-pole problem.
Transactions of the Society of Mechanical Engineers
Transactions of the Japan Society of Mechanical Engineers
Transactions of the Japan Society of Mechanical Engineers Series A
Transactions of the Japan Society of Mechanical Engineers Series B
TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C
TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series B
TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series A
