Integrated structure and parameter selection for eng-genes neural models

Authors:
Patrick Connally;Kang Li;George W. Irwin
Affiliations:
Intelligent Systems and Control Research Group, Queen’s University Belfast, Belfast, UK;Intelligent Systems and Control Research Group, Queen’s University Belfast, Belfast, UK;Intelligent Systems and Control Research Group, Queen’s University Belfast, Belfast, UK
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Abstract

A new approach to the construction and optimisation of ‘eng-genes’ grey-box neural networks is investigated. A forward selection algorithm is used to optimise both the network weights and biases and the parameters of the system-derived activation functions. The algorithm is used for both conventional neural network and eng-genes modelling of a simulated Continuously Stirred Tank Reactor. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes.