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Combat multiple access interference(MAI) and near-far effect, to some degree, BP neural network can be better suppression interference, because of its use of the least squares method, prone to local minimum. In this paper, instead of using the MMSE algorithm for least squares and the addition of a new kind of constraints, and gain iterative formula of BP neural network blind multiuser detection algorithm,...
This paper briefly analyses the conventional reactive power optimization compensation. The new method proposes a new optimization reactive power compensation for electrical network that uses neural network to predict electric network's important parameters and nonlinear prime-dual interior algorithm to optimize reactive power. This intelligent control system diminishes power losses, and settles the...
Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos...
In analyzing the nonlinearity characteristics and strong interference of traffic flow parameters, a new approach has been proposed for the prediction of traffic flow parameters. First, multi-scale analysis is used to decompose the sequences of traffic flow parameters into the low and high frequency ones and restore them according to the reconstruct principle of wavelet coefficients. Then artificial...
Considering the problem of the local optimization in the adaptive genetic algorithm (AGA), this paper presents an improved adaptive genetic algorithm (IAGA) which can optimize the weights and thresholds of the neural network. A stock prediction system based on neural networks and fuzzy theory is designed. According to the analysis of the history data of the stock, the system predicts this stock's...
As the traditional ways of MPPT suffers from some dissatisfaction, this paper presents a kind of Fuzzy Support Vector Machines (FSVM) for the prediction and simulation of the voltage of the maximum power output in PV module. The measured data of meteorological light intensity was used to simulate and analyze shows that Compared with BP Neural Network, the model of FSVM achieved the minimization principle...
This paper improves the basic particle swarm optimization (PSO) algorithm with adaptive interior and acceleration coefficients which is called IPSO, and use the IPSO algorithm to optimize authority value and threshold value of BP nerve network. Thus IPSO-BP neural network algorithm model has been established and applied into the railway passenger volume forecast. The result shows that this model has...
Stochastic optimization problems widely exist in engineering, management, control and many other fields. In order to search a more effective algorithm for solving these problems, generalized regression neural network is used as a fitness prediction model and an intelligent algorithm which combines generalized regression neural network with particle swarm optimization is presented. In this intelligent...
In allusion to the problems that the conventional wavelet neural network has disadvantages of training slowly, convergence to the local minimum easily and poor approximation performance, two aspects including initial parameters selection and network training methods were selected to be optimized after analyzing its approximation performance. A kind of self-adaptive method to get the number of hidden...
According to the problems of the nonlinearity and non norm on dam displacement prediction, the dam displacement mode based on improved ant colony algorithm neural networks was proposed. The binary ant colony algorithm has been brought into the optimization of weights in neural networks. So that the shortcomings of the ant algorithm using in the combinatorial optimization in continuous field have been...
This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training...
The BP neural network algorithm has characteristics of slow convergence speed and local minimum value which could cause the loss of global optimal solution. In order to eliminate the shortcoming of BP neutral network algorithm, genetic algorithm is been put forward to optimize authority value and threshold value of BP nerve network. This paper establishes genetic neural network model. Study has been...
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