Due to their powerful optimization property, genetic algorithms (GA's) are
currently being investigated for the development of adaptive or self-tuning
fuzzy logic control systems. This paper presents a neuro-fuzzy logic contr
oller (NFLC) where all of its parameters can be tuned simultaneously by GA.
The structure of the controller is based on the radial basis function neur
al network (RBF) with Gaussian membership functions. The NFLC tuned by GA c
an somewhat eliminate laborious design steps such as manual tuning of the m
embership 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 is compared with a conventional fuzzy controller and a
PLD controller tuned by GA. Simulation results show that the proposed contr
oller offers encouraging advantages and has better performance.