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An approach that neural network optimized with PSO algorithm is proposed in the paper. Unlike conventional training method with gradient descent method only, this paper introduces a hybrid training algorithm by combining the PSO and BP algorithm. The PSO is used to optimize the initial parameters of the BP neural network, including the weights and biases. It can effectively better the cases that network...
The BP neural network has proven robust even for complex nonlinear problems. However, its high performance results are attained at the expense of a long training time to adjust the network parameters, which can be discouraging in many real-world applications. Even on relatively simple problems, standard BP often requires a lengthy training process in which the complete set of training examples is...
In the paper, we constructed the response surface mold through the neural network, elaborated the concept and mathematics description of the response surface. Through a real example, we solved the coupling relationship during multidisciplinary as well as the synthesis coordination of many disciplines with multidisciplinary design based on the response surface. We completed the data exchanging and...
In traditional BP algorithm, sigmoid activation function outputs are restricted in interval [0, 1], and BP algorithm converges slow and has very low accuracy near 0 and 1. A new sigmoid activation function is put forward and applied to the forecast of short-term traffic flow. Simulation shows that the proposed activation function has overcome the above two shortcomings and got better effect than the...
This paper proposed an algorithm of feature selection used in fusion of soft computing and based on the chain of data-information-cognition. The algorithm is as follow: Firstly, the weights wij from input layer to hidden layer are obtained when the training accuracy of BP neural network (BPNN) is got. Where i denotes the i th feature and j denotes the j th node in hidden layer of BPNN. Secondly, zeta...
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