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V. T. S. Elanayar and C. S. Yung, “Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic,” IEEE Transactions on Neural Networks, Vol. 5, No. 4, 1994, pp. 594-603. doi:10.1109/72.298229

has been cited by the following article:

• TITLE: A Comparative Study of Nonlinear Time-Varying Process Modeling Techniques: Application to Chemical Reactor

  AUTHORS: Errachdi Ayachi, Saad Ihsen, Benrejeb Mohamed

  KEYWORDS: Nonlinear Systems; Time-Varying Systems; Multi Layer Perceptron; Radial Basis Function; Gradient Descent; Genetic Algorithms; Optimization

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.1, February 28, 2012

  ABSTRACT: This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.