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This paper considers the distributed robust consensus problem of multi-agent systems with Lipschitz nonlinear dynamics and subject to different matching uncertainties. Due to the existence of nonidentical uncertainties, the multi-agent systems discussed in this paper are essentially heterogeneous. For the case where the communication graph is undirected and connected, based on the local state information...
ANN (the artificial neural network) is applied on the products from the numerical model of WRF (the Meso-Scale Weather Research Forecast Model) for forecasting categorical Rainfall over lower Yangtse river Valley during Meiyu season. The products are as the basic forecast factors, then they are united into combined factors with atmospheric dynamic, thermodynamic and humidity features. The combined...
The price of coal and electricity depends on various indeterminate factors, and there is a very complicated coupling relationship among them. The forecasting model becomes more complex for their strong nonlinear features that bring lots of difficulties in constructing a precise forecasting model with the traditional methods. This paper proposes a new method, on the basis of PSO and RBF neural network,...
To analyze the composition factors of difference between produce and sale (DPS) scientifically, the composition of DPS are analyzed. the measurement techniques of influence factors such as leakage rate of city pipeline, leakage rate of water second supply within inner pipeline, self usage percentage of pipeline system running, usage percentage of fire fighting, sale lose and commonweal usage percentage,...
Trade barriers correct externalities, but distort trade as well, which brings negative effect on international distribution of resources. Study on how to recognize there are trade barriers or would-be threat of trade barriers in international trade is of great necessity. Based on previous studies, taking horticultural products as an example, this paper designs a general RBF neural network model to...
The traditional prediction model is not able to achieve a satisfying prediction effect in the problem of a non-linear system and nonstationary financial signal. The existing wavelet neural network has overcome the deficiency of traditional prediction model which is limited to linear system when predicting. However, wavelet neural network has a defect of confusing signal frequency. Based on the theory...
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