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Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps

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Fuzzy Cognitive Maps for Applied Sciences and Engineering

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 54))

Abstract

This chapter is devoted to the synthesis and some analysis of multi-step learning algorithms for fuzzy cognitive maps (FCM). Multi-step supervised learning based on gradient method and unsupervised learning type of differential Hebbian learning (DHL) algorithm were described. Comparative analysis of these methods to one-step algorithms, from the point of view of the influence on the work of the medical prediction system (average percentage prediction error) was performed. FCM learning and testing was based on historical data. Simulation research together with the analysis results were done on prepared software tool ISEMK (Intelligent Expert System based on Cognitive Maps). Selected results of this analysis were presented.

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Correspondence to Katarzyna Piotrowska .

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Yastrebov, A., Piotrowska, K. (2014). Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps. In: Papageorgiou, E. (eds) Fuzzy Cognitive Maps for Applied Sciences and Engineering. Intelligent Systems Reference Library, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39739-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-39739-4_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39738-7

  • Online ISBN: 978-3-642-39739-4

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