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PROCESS IDENTIFICATION THROUGH MODULAR NEURAL NETWORKS AND RULE EXTRACTION

By Berend Jan, Van Der Zwaag, Kees Slump and Lambert Spaanenburg

Abstract

Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and – if at all – hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule extraction is performed module by module. This has all the benefits of a divideand-conquer method and opens the way to structured design. This paper discusses a next step in this direction by illustrating the potential of base functions to design the neural model.

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.199.8065
Provided by: CiteSeerX
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