MLP Neural Networks Optimization through Simulated Annealing in a Hybrid Approach for Time Series Prediction

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks for time series prediction. Experimental results have shown that this approach selects the appropriate time series lags and builds an MLP with adequate number of hidden neurons required for achieving good performance on the task. The performance attained was better than some results recently reported for hybrid systems combining Genetic Algorithms (GA) and MLPs for the same purpose presented here.