Unsupervised adaptive neural-fuzzy inference system for solving differential equations
Applied Soft Computing
Supervised neuro-fuzzy clustering for life science applications
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
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In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary.In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data.