文档介绍：To be published in the IEEE Transactions on Systems, Man and ics
April 1999
TUNING OF A NEURO-FUZZY CONTROLLER
BY IC ALGORITHM
Teo Lian Seng, Marzuki Khalid*, and Rubiyah Yusof
Centre for Artificial Intelligence and Robotics,
University Teknologi Malaysia,
Jalan Semarak, 54100 Kuala Lumpur, Malaysia.
Email address: ******@
Fax number: 603-2904892
(All correspondence should be sent to *)
ABSTRACT
Due to their powerful optimization property, ic algorithms (GAs) are currently
being investigated for the development of adaptive or self-tuning fuzzy logic control systems.
This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be
tuned simultaneously by GA. The structure of the controller is based on the Radial Basis
Function work (RBF) with Gaussian membership functions. The NFLC tuned by GA
can somewhat eliminate laborious design steps such as manual tuning of the membership
functions and selection of the fuzzy rules. The GA implementation incorporates dynamic
crossover and mutation probabilistic rates for faster convergence. A flexible position coding
strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The
performance of the proposed controller pared with a conventional fuzzy controller and a
PID controller tuned by GA. Simulation results show that the proposed controller offers
encouraging advantages and has better performance.
1. INTRODUCTION
Fuzzy logic control systems, which have the capability of transforming linguistic
information and expert knowledge into control signals [1-2], are currently being used in a wide
variety of engineering applications [3-7]. The simplicity of designing these fuzzy logic systems
has been the main advantage of their essful implementation over traditional approaches
such as optimal and adaptive control techniques. Despite the advantages of the conventional
fuzzy logic controller ( FLC ) over traditional approaches