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- W2350765899 abstract "To accelerate the convergence speed of the BP neutral network and avoid to getting into the local minimum, a novel variant activation (transform) sigmoid function with three parameters is proposed in this paper, then the improved BP learning algorithm based on it is deduced, furthermore, we combined the new fast BP algorithm and chaos optimization algorithm, the learning of the new algorithm converges fast and globally with no local minimum. The faster convergence speed and higher prediction precision of the improved algorithm compared with the traditional BP ANNs is proved by the results of simulation tests of nonlinear function and the prediction results of short-term load." @default.
- W2350765899 created "2016-06-24" @default.
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- W2350765899 date "2005-01-01" @default.
- W2350765899 modified "2023-09-25" @default.
- W2350765899 title "Short Term Load Forecasting by Using Neural Networks with Variable Activation Functions and Embedded Chaos Algorithm" @default.
- W2350765899 hasPublicationYear "2005" @default.
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