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For the shortcoming of Particle Swarm Optimization (PSO) algorithm in Wavelet Neural Network (WNN) training, a modeling approach of WNN based on improved PSO algorithm is proposed. The approach applied a PSO algorithm based on the strategies of multi-particle information sharing and self-adaptive inertia weight to optimize the parameters of WNN for modeling quality of WNN. The experiment result indicates...
This paper proposes a wavelet neural networks (WNN) with self-adaptive learning rate. The algorithm can automatically change the learning rate with operational parameter, but without any artificial adjustments. Thus it once for ado overcomes the drawbacks of WNN, i. e. slow convergence, inability to determine the value of learning rate and easiness to fall into local minimum point. The results of...
A novel method named wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for simultaneous determination of Co (II), Zn (II) and Cu (II) by combining wavelet packet denoising with Elman recurrent neural network. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal can...
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