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Published in:
2014 | OriginalPaper | Chapter
Structure Perturbation Optimization for Hopfield-Type Neural Networks
Authors : Gang Yang, Xirong Li, Jieping Xu, Qin Jin
Published in: Artificial Neural Networks and Machine Learning – ICANN 2014
Publisher: Springer International Publishing
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In this paper, we extract the core idea of state perturbation from
Hopfield-type
neural networks and define state perturbation formulas to describe the general way of optimization methods. Departing from the core idea and the formulas, we propose a novel optimization method related to neural network structure, named structure perturbation optimization. Our method can produce a structure transforming process to retrain
Hopfield-type
neural networks to get better problem-solving ability. Experiments validate that our method effectively helps
Hopfield-type
neural networks to escape from local minima and get superior solutions.