Beyond Backpropagation: Using Simulated Annealing for Training Neural Networks

Beyond Backpropagation: Using Simulated Annealing for Training Neural Networks

Randall S. Sexton (Ball State University, USA), Robert E. Dorsey (University of Mississippi, USA) and John D. Johnson (University of Mississippi, USA)
Copyright: © 1999 |Pages: 8
DOI: 10.4018/joeuc.1999070101
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Abstract

The vast majority of neural network research relies on a gradient algorithm, typically a variation of backpropagation, to obtain the weights of the model. Because of the enigmatic nature of complex nonlinear optimization problems, such as training artificial neural networks, this technique has often produced inconsistent and unpredictable results. To go beyond backpropagation’s typical selection of local solutions, simulated annealing is suggested as an alternative training technique that will search globally. In this research, backpropagation will be directly compared with this global search technique via an intensive Monte Carlo study on seven test functions.

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