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2014 | OriginalPaper | Buchkapitel

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

verfasst von : Alexander Yastrebov, Katarzyna Piotrowska

Erschienen in: Fuzzy Cognitive Maps for Applied Sciences and Engineering

Verlag: Springer Berlin Heidelberg

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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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Vorheriges Kapitel Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series

Nächstes Kapitel Designing and Training Relational Fuzzy Cognitive Maps

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Titel: Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps
verfasst von: Alexander Yastrebov
Katarzyna Piotrowska
Verlag: Springer Berlin Heidelberg
Buch: Fuzzy Cognitive Maps for Applied Sciences and Engineering
Print ISBN: 978-3-642-39738-7

Electronic ISBN: 978-3-642-39739-4

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-642-39739-4_8

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