Theory and modeling for heterogeneous polynomial neural networks
Abstract
We consider different mathematical models, architectures and methods for learning, selforganization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of vector (widened) pattern recognition, data classification and diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows.References
Published
2007-08-01
How to Cite
Timofeev,. (2007). Theory and modeling for heterogeneous polynomial neural networks. SPIIRAS Proceedings, (4), 73-86. https://doi.org/10.15622/sp.4.4
Section
Articles
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