Application of Shuffled Frog-Leaping Algorithm Based Neural Network in Harmonic Measuring
According to the harmonic measuring for traditional BP neural network, compares the problems of slow convergence speed, easily falling into local minimum value. Proposes a Shuffled Frog-leaping algorithm neural network using Shuffled Frog-leaping Algorithm, instead of a Gradient Search Algorithm in BP neural network method for Harmonic amplitude and phase measurements of power of system. The neural network model is developed according to the requirements of measuring harmonic. Expounds the basic principle of Shuffled Frog-leaping Algorithm neural network. Gives the training method of SFLA neural network and how to construct the training sample in the three har-monic as an example. The simulation results verify the feasibility of the proposed method. SFLA neural network convergence speed and detection accuracy is better than the BP neural network. Uses the neural network detection trained without training samples, the result proves that the neural network has good generalization ability.
NETL
NSTL
万方数据
维普
