Title
Weights And Structure Determination Of Pruning-While-Growing Type For 3-Input Power-Activation Feed-Forward Neuronet
Abstract
In this paper, a new type of 3-input power activation feed-forward neuronet (3IPFN) is constructed and investigated. For the 3IPFN, a novel weights-and-structure determination (WASD) algorithm is presented to solve data approximation and prediction problems. With the weights-direct-determination (WDD) method exploited, the WASD algorithm can obtain the optimal weights of the 3IPFN between hidden layer and output layer directly. Moreover, the WASD algorithm determines the optimal structure (i.e., the optimal number of hidden-layer neurons) of the 3IPFN adaptively by growing and pruning hidden-layer neurons during the training process. Numerical results of illustrative examples highlight the efficacy of the 3IPFN equipped with the so-called WASD algorithm.
Year
DOI
Venue
2012
10.1109/ICAL.2012.6308199
2012 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS (ICAL)
Keywords
DocType
Volume
Pruning-while-growing, feed-forward neuronet, weights-and-structure-determination (WASD) algorithm, optimal structure, approximation
Conference
null
Issue
ISSN
Citations 
null
2161-8151
0
PageRank 
References 
Authors
0.34
8
5
Name
Order
Citations
PageRank
Yunong Zhang12344162.43
Wenchao Lao2113.27
Yonghua Yin3697.58
Lin Xiao456242.84
Jinhao Chen5122.33