Abstract
In this paper dynamic global models of input-output complex systems are discussed. Dynamic complex system which consists of two nonlinear discrete time sub-systems is considered. Multilayer neural networks in a dynamic structure are used as a global model. The global model is composed of two sub-models according to the complex system. A quality criterion of the global model contains coefficients which define the participation of sub-models in the global model. The main contribution of this work is the influence study on the global model quality of these coefficients. That influence is examined for different back propagation learning algorithms for complex neural networks.
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Drałus, G. (2012). Complex Neural Models of Dynamic Complex Systems: Study of the Global Quality Criterion and Results. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds) Human – Computer Systems Interaction: Backgrounds and Applications 2. Advances in Intelligent and Soft Computing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23172-8_31
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DOI: https://doi.org/10.1007/978-3-642-23172-8_31
Publisher Name: Springer, Berlin, Heidelberg
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