NDLI: Mixed transfer function neural networks for knowledge acquisition

Content Provider	IEEE Xplore Digital Library
Author	Khan, M.I. Frayman, Y. Nahavandi, S.
Copyright Year	2009
Description	Author affiliation: Institute of Technology Research and Innovation (ITRI), Deakin University at Burwood Campus, Elgar Road, VIC, 3125, Australia (Frayman, Y.) \|\| Institute of Technology Research and Innovation (ITRI), Deakin University, Waurn Ponds Campus, Geelong 3217, Australia (Khan, M.I.; Nahavandi, S.)
Abstract	Modeling helps to understand and predict the outcome of complex systems. Inductive modeling methodologies are beneficial for modeling the systems where the uncertainties involved in the system do not permit to obtain an accurate physical model. However inductive models, like Artificial Neural Networks (ANNs), may suffer from a few drawbacks involving over-fitting and the difficulty to easily understand the model itself. This can result in user reluctance to accept the model or even complete rejection of the modeling results. Thus, it becomes highly desirable to make such inductive models more comprehensible and to automatically determine the model complexity to avoid over-fitting. In this paper, we propose a novel type of ANN, a Mixed Transfer Function Artificial Neural Network (MTFANN), which aims to improve the complexity fitting and comprehensibility of the most popular type of ANN (MLP - a Multilayer Perceptron).
Starting Page	1
Ending Page	6
File Size	268838
Page Count	6
File Format	PDF
ISBN	9781424435067
DOI	10.1109/ICIT.2009.4939662
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transfer functions Neural networks Knowledge acquisition Artificial neural networks Neurons Multilayer perceptrons Technological innovation Australia Electronic mail Uncertainty model complexity inductive modeling neural networks mixed transfer functions over-fitting
Content Type	Text
Resource Type	Article

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