Optimization of Neural Networks Using Variable Structure Systems


Mohseni, , Seyed Alireza and Tan, , Ai Hui (2012) Optimization of Neural Networks Using Variable Structure Systems. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 42 (6). pp. 1645-1653.

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This paper proposes a new mixed training algorithm consisting of error backpropagation (EBP) and variable structure systems (VSSs) to optimize parameter updating of neural networks. For the optimization of the number of neurons in the hidden layer, a new term based on the output of the hidden layer is added to the cost function as a penalty term to make optimal use of hidden units related to weights corresponding to each unit in the hidden layer. VSS is used to control the dynamic model of the training process, whereas EBP attempts to minimize the cost function. In addition to the analysis of the imposed dynamics of the EBP technique, the global stability of the mixed training methodology and constraints on the design parameters are considered. The advantages of the proposed technique are guaranteed convergence, improved robustness, and lower sensitivity to initial weights of the neural network.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 28 Dec 2012 07:15
Last Modified: 28 Dec 2012 07:15
URII: http://shdl.mmu.edu.my/id/eprint/3734


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