Evolutionary polymorphic neural network in chemical process modeling

Document Type

Article

Publication Date

11-15-2001

Abstract

Evolutionary polymorphic neural network (EPNN) is a novel approach to modeling dynamic process systems. This approach has its basis in artificial neural networks and evolutionary computing. As demonstrated in the studied dynamic CSTR system, EPNN produces less error than a traditional recurrent neural network with a less number of neurons. Furthermore, EPNN performs networked symbolic regressions for input-output data, while it performs multiple step ahead prediction through adaptable feedback structures formed during evolution. In addition, the extracted symbolic formulae from EPNN can be used for further theoretical analysis and process optimization. © 2001 Elsevier Science Ltd. All rights reserved.

Identifier

0035890807 (Scopus)

Publication Title

Computers and Chemical Engineering

External Full Text Location

https://doi.org/10.1016/S0098-1354(01)00708-6

ISSN

00981354

First Page

1403

Last Page

1410

Issue

11-12

Volume

25

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