Abstract
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.
This research was partially supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 14XNLQ01), and the grants from the Natural Science Foundation of China (No. 61303184).
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Yang, G., Li, X., Xu, J., Jin, Q. (2014). Structure Perturbation Optimization for Hopfield-Type Neural Networks. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_39
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DOI: https://doi.org/10.1007/978-3-319-11179-7_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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