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Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm
1Department of Information Engineering, School of Computer Science and Engineering, The University of New South Wales, Sydney N.S.W. 2052, Australia.
IEEE Transactions on Neural Networks
|February 7, 2008
Summary
This study introduces SARPROP, a novel algorithm combining gradient descent with simulated annealing (SA) to prevent neural networks from getting stuck in local minima. SARPROP enhances training efficiency and performance by escaping poor solutions faster than traditional methods.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Neural Networks
Background:
- Gradient descent algorithms, like Resilient Backpropagation (RPROP), can converge to suboptimal local minima during neural network training.
- Global optimization techniques can escape local minima but significantly increase training times.
Purpose of the Study:
- To investigate the combination of gradient descent and simulated annealing (SA) for improved neural network training.
- To develop an algorithm that overcomes local minima without sacrificing training efficiency.
Main Methods:
- Simulated annealing (SA) was integrated into the Resilient Backpropagation (RPROP) algorithm, creating a new method called SARPROP.
- SARPROP incorporates SA through noise and weight decay mechanisms.
- A restart training phase was introduced for more comprehensive error surface exploration and automatic annealing schedules.
Main Results:
- SARPROP effectively escapes local minima in feedforward neural networks.
- The algorithm maintains or even improves upon the training times of the original RPROP algorithm.
- The restart training phase enhances the thoroughness of the error surface search.
Conclusions:
- SARPROP offers a superior approach to training neural networks by mitigating the local minima problem.
- The combined gradient descent and simulated annealing strategy enhances both performance and training speed.
- SARPROP presents a promising advancement for efficient and effective neural network optimization.
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