ADAPTATION MOMENTUM FACTOR AND STEEPNESS PARAMETER IN BACK PROPAGATION ALGORITHM USING FIXE STRUCTURE LEARNING AUTOMATA

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BEYGI H.; MEYBODI M.R.

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Journal: Scientia Iranica Year:2001 | Volume:8 | Issue:4 (COMPUTER ENGINEERING) Page(s): 250-264

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Title

ADAPTATION MOMENTUM FACTOR AND STEEPNESS PARAMETER IN BACK PROPAGATION ALGORITHM USING FIXE STRUCTURE LEARNING AUTOMATA

Author(s)

BEYGI H. | MEYBODI M.R. | Issue Writer Certificate

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Abstract

Back propagation (BP) algorithm is a systematic method for training multi-layerneural networks, which, despite many successful applications. also has many drawbacks. For complex problems, back propagation may requie a long time to train the networks and it is possible that no training occurs at all. Long train in time can be the result of non-optimal parameters. It is not easy to choose an appropriate value for the parameters of a particular problem and the parameters are usually determined by rail and error. If the parameters are not chosen appropriately, slow convergence paralysis and continuous instability can result [1-4]. Moreover, the best values for the parameters at the beginning of training may not be good enough later. In this paper A technique has been incorp rated into BP algorithm for adaptation of steepness parameter and momentum factor in order to achieve a higher rate of convergence. Through interconnection of Fixed Structure Learn in Automata (FSLA) to the feed forward neural networks. Learning automata scheme is applied in order to adjust these parameters based on the observation of random response of neural networks. The main motivation in using learning automata as an adaptation algorithm is in is capability of global optimization when dealing with multi-modal surfaces. The feasibility of he proposed method is shown through simulations on three learning problems: exclusive-or encoding problem and digit recognition. These problems are chosen because they have different error surfaces and collectively present an environment that is suitable to determine the effect of the proposed method. The simulation results show that the adaptation of these parameters using his method increases not only the convergence rate of learning but also the likelihood of escaping the local minima. Computer simulations provided in this paper indicate that at least a magnitude of savings in running time can be achieved when FSLA is used for the adaptation of momentum factor and steepness parameters. Furthermore simulations demonstrate that the FSLA approach performs much better than the Variable Structure Learning Automata (VSLA) approach reported in [1,2].

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APA: Copy

BEYGI, H., & MEYBODI, M.R.. (2001). ADAPTATION MOMENTUM FACTOR AND STEEPNESS PARAMETER IN BACK PROPAGATION ALGORITHM USING FIXE STRUCTURE LEARNING AUTOMATA. SCIENTIA IRANICA, 8(4 (COMPUTER ENGINEERING)), 250-264. SID. https://sid.ir/paper/289680/en

Vancouver: Copy

BEYGI H., MEYBODI M.R.. ADAPTATION MOMENTUM FACTOR AND STEEPNESS PARAMETER IN BACK PROPAGATION ALGORITHM USING FIXE STRUCTURE LEARNING AUTOMATA. SCIENTIA IRANICA[Internet]. 2001;8(4 (COMPUTER ENGINEERING)):250-264. Available from: https://sid.ir/paper/289680/en

IEEE: Copy

H. BEYGI, and M.R. MEYBODI, “ADAPTATION MOMENTUM FACTOR AND STEEPNESS PARAMETER IN BACK PROPAGATION ALGORITHM USING FIXE STRUCTURE LEARNING AUTOMATA,” SCIENTIA IRANICA, vol. 8, no. 4 (COMPUTER ENGINEERING), pp. 250–264, 2001, [Online]. Available: https://sid.ir/paper/289680/en

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