Summary
In this paper using a penalty function to obtain satisfactory local models in global modeling of complex systems using neural networks are discussed. Complex systems consist of several subsystems in a cascade structure. As models, non-typical neural multilayer feedforward networks were used. To formulate the global performance index the global resultant criterion, which consist a global error criterion and a penalty function for local errors, approach is proposed. Global resultant criterion required developing of a new backpropagation learning algorithm. The computer simulation results of modelling of the chosen complex system are presented.
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Drałus, G., Świątek, J. (2003). A Penalty Function to Obtain Satisfactory Local Models of Complex Systems. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_22
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_22
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
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