ODE-LM: A Hybrid Training Algorithm for Feedforward Neural Networks
A hybrid training algorithm named ODE-LM, in which the orthogonal differential evolution (ODE) algorithm is combined with the Levenberg-Marquardt (LM) method, is proposed to optimize feedforward neural network weights and biases. The ODE is first applied to globally optimize the network weights in a large space to some extent (the ODE will stop after a certain generation), and then LM is used to further learn until the maximum number of iterations is reached. The performance of ODE-LM has been evaluated on several benchmarks. The results demonstrate that ODE-LM is capable to overcome the slow training of traditional evolutionary neural network with lower learning error.
KeywordsFeedforward neural network Differential evolution Orthogonal crossover Levenberg-Marquardt method
This work was supported by the Fundamental Research Funds for the Central Universities (K50511700004).
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