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Adaptive Simulated Annealing: An Alternative Approach For The Error Minimization Of Neural Networks, Yuxing Sun
Adaptive Simulated Annealing: An Alternative Approach For The Error Minimization Of Neural Networks, Yuxing Sun
Masters Theses
This work introduces an alternative algorithm, simulated annealing, to minimize the prediction error from neural networks that traditionally use back-propagation methods. The simulated annealing algorithm stochastically samples the parameter space formed by weights of the neural network until a minimal error is found.
Three problems were investigated in this work: the radiator problem, the spiral problem, and the time series prediction problem. Each of them was examined using the same neural network architecture, i.e., a 2-layer network, and trained by both back-propagation and simulated annealing.
The simulated annealing algorithm consumes longer computation time in searching for the global minimum than …