Application of simulated annealing to the backpropagation model improves convergence

Charles B. Owen; Adel M. Abunawass

doi:10.1117/12.152626

19 August 1993 Application of simulated annealing to the backpropagation model improves convergence

Charles B. Owen, Adel M. Abunawass

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152626
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

The Backpropagation technique for supervised learning of internal representations in multi- layer artificial neural networks is an effective approach for solution of the gradient descent problem. However, as a primarily deterministic solution, it will attempt to take the best path to the nearest minimum, whether global or local. If a local minimum is reached, the network will fail to learn or will learn a poor approximation of the solution. This paper describes a novel approach to the Backpropagation model based on Simulated Annealing. This modified learning model is designed to provide an effective means of escape from local minima. The system is shown to converge more reliably and much faster than traditional noise insertion techniques. Due to the characteristics of the cooling schedule, the system also demonstrates a more consistent training profile.

Citation Download Citation

Charles B. Owen and Adel M. Abunawass "Application of simulated annealing to the backpropagation model improves convergence", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152626

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