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Knowledge Extraction from a Mixed Transfer Function Artificial Neural Network


M. Imad Khan, Yakov Frayman, and Saeid Nahavandi


Intelligent Systems Research Group, School of Engineering and Information Technology, Deakin University, Waurn Ponds, Geelong, VIC 3217, Australia


Received: February 22, 2005

Accepted: December 21, 2005


Keywords: neural networks, mixed transfer functions, knowledge extraction

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.3 pp. 295-301, 2006

Abstract



One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.
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