Due to their powerful optimization property, genetic 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 neural network (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 is compared 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.
This paper proposes an integrated general regression neural network (GRNN ) adaptation scheme for dynamic plant modelling. The scheme can be used in a noisy and dynamic environment for on-line process control. It possesses several distinguished features compared with the original GRNN proposed by Specht, such as a flexible pattern nodes add-in and delete-off mechanism, a dynamic initial sigma assignment using a non-statistical method, automatic target adjustment and sigma tuning. These adaptation strategies are formulated on the basis of the inherent advantageous features found in GRNN, such as highly localized pattern nodes, good interpolation capability and instantaneous learning. Good modelling performance was obtained when the GRNN is tested on a linear plant in a noisy environment. It performs better than the well-known extended recursive leastsquares identification algorithm. In this paper, analysis of the effects of some of the adaptation parameters involving a non-linear plant is also investigated. The results show that the proposed methodology is computationally efficient and exhibits several attractive features such as fast learning, flexible network sizing and good robustness, which are suitable for the construction of estimators or predictors for many model-based adaptive control strategies.
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