论文信息 - Optimizing Weight and Threshold of BP Neural Network Using SFLA: Applications to Nonlinear Function Fitting

Optimizing Weight and Threshold of BP Neural Network Using SFLA: Applications to Nonlinear Function Fitting

The shuffled frog-leaping algorithm (SFLA) is presented along with a pseudocode and flow chart to facilitate its implementation. And then we use SFLA to optimize the weight and the threshold value of BP network. Based on the experiments, we show that SFLA performs better than Genetic Algorithms (GAs) in the optimization of BP network's weight and threshold value, which are used in the nonlinear function fitting.

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