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Simultaneous Perturbation Stochastic Approximation in global optimisation

Patan K.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2006

z. 156

343-350

The paper deals with global property analysis of the Simultaneous Perturbation Stochastic Approximation algorithm. It spite of the fact that the algorithm uses an estimation of a gradient, a global optimisation property can be achieved by using properly shaped sequence {ck} used for generating trial points. The experiments are performed on a number of test functions showing efficiency of the method. The examined algorithm is also used to train a dynamic neural network. A network under consideration is designed using neuron models with internal dynamice. The identification of an industrial process based on real data shows usefullness of the algorithm.