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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...
A improved gradient-based backpropagation training method is proposed for neural networks in this paper. Based on the Barzilai and Borwein steplength update and some technique of Resilient Propagation method, we adapt the new learning rate to improves the speed and the success rate. Experimental results show that the proposed method has considerably improved convergence speed, and for the chosen test...
We present a new method to optimize weights of artificial neural network (ANN) with particle swarm optimization (PSO), also we propose a new selection strategy of inertial weight, which varies according to the training error of artificial neural network, called adaptive inertial weight. By using Adaptive inertial weight, the proposed method can search global optimal solution faster and exactly. The...
To accomplish the demand of continuously variable transmission (CVT) fault diagnosis, the structure of CVT fault diagnosis system is built and the application model of Back-Propagation Neural Network is established aiming at the features of CVT faults. The structure and 3 algorithms of network are devised. The network proposed is simulated and the results are analyzed in detail. The simulation results...
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...
The paper is given a new modified differential evolution (MDE) algorithm in which a novel mutation operator is introduced. The MDE algorithm can obtain a good balance between global search and local search and was applied in BP neural network training. The numerical results demonstrate that the new MDE algorithm has the abilities of good global search and faster convergence speed and higher convergence...
In this paper, we proposed a method which incorporated multi-scale analysis into neural nets to solve the problem that fractal coding allows fast decoding but suffers from long encoding times. This method can reduce the computational load of fractal image coding significantly though efficient classification of image improve speed of image scan. Furthermore this paper also incorporates gray relational...
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