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In this paper, an adaptive radial basis function (RBF) neural-networks (NNs) control algorithm is developed for a class of nonlinear affine systems based on data from controller with constrained control inputs. First, a novel nonquadratic performance index functional is introduced to overcome the nonlinear control constraints, and then the iterative adaptive optimal control algorithm is developed...
Based on contraction mapping principle, inequality technique, global exponential stability of a class of BAM neural networks with distributed delays is considered. Some sufficient conditions are derived which ensure the existence, uniqueness, global exponential stability of equilibrium points of the neural networks. Finally, the obtained results are demonstrated with a numerical example.
Aiming at the BP artificial neural network unable to auto select and optimize input variables, this paper integrates BPANN with grey relational analysis method, establishes an optimized BP artificial neural network arithmetic (GM2BPANN) which based on the grey relational analysis method. The hybrid approach has been used to forecasting the online item price. The result shows that the new model can...
P2P traffic has become one of the most significant portions of the network traffic. How to improve the accuracy of the traffic identification efficiently is still a difficult problem. A promising approach that has recently received some attention is traffic classification using machine learning techniques. In this paper, we propose a BP neural network algorithm for P2P traffic classification problem...
In this paper, the establishment of the neural network model of forecasting short-term power load in an electric power grid is studied. Basing on the model, the BP algorithm for power load is explored. The research on BP network model includes determining the hidden layer number, hidden layer nodes number, training frequency and accuracy of learning rate. In this paper, we focus on that how to give...
Considering the chaotic characteristic of power system load, a method based on bee evolution modifying particle swarm optimization (BEMPSO) and chaotic neural network is presented for power system load forecasting to improve precision. In this paper, builds the chaotic neural network model and integrates bee evolution modifying with particle swarm optimization. The novel BEMPSO algorithm is proposed...
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