Simultaneous Perturbation Stochastic Approximation in global optimisation

• Autorzy: Patan K.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

global optimization; stochastic approximation; Simultaneous Perturbation Stochastic Approximation; SPSA; neural networks

PL

optymalizacja globalna; aproksymacja stochastyczna; sieci neuronowe

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 343--350
• Opis fizyczny: Bibliogr. 10 poz., wykr.

Twórcy

autor

Patan K.

• Institute of Control and Computation Engineering, University of Zielona Góra ul. Podgórna 50, 64-246 Zielona Góra, Poland,

Bibliografia

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• [3] J. Korbicz, K. Patan and A. Obuchowicz. Dynamic neural networks for process modelling in fault detection and isolation systems. International Journal of Applied Mathematics and Computer Science, 9(3):519-546, 1999.
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• [5] A. Obuchowicz. Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis. University of Zielona Góra Press, Zielona Góra, Poland, 2003.
• [6] K. Patan and J. Korbicz. Application of Dynamic Neural Networks in an Industrial Plant. In Proc. Int. Symp. Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS 2000, Budapest, Hungary, pages 186-191, 2000.
• [7] K. Patan and T. Parisini. Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process. Journal of Process Control, 15:67-79, 2005.
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• [9] J.C. Spall. Multivariate Stochastic aproximation using a simultaneous perturbation gradient approximation. IEEE Trans. Automatic Control, (37):332-341, 1992.
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