Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

Veronica Chan and Christine Chan
Additional contact information
Veronica Chan: University of Regina, Regina, Canada
Christine Chan: University of Regina, Faculty of Engineering and Applied Science, Regina, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 2, 57-73

Abstract: This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJCINI.2017040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:11:y:2017:i:2:p:57-73

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().