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This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-organizing capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so as not only to avoid the problem inherent in the NN with fixed structure...
In this paper, a new measure of correlation is introduced in undirected network. In order to get accurately degree distribution for the vertex at the end of a randomly chosen edge, we considered the information of edge, rather than just the degree distribution of node. We analysis the Enron Email Network with the three measures via degree-degree correlation, and get more information than the traditional...
An indirect adaptive control approach based on LS-SVM is proposed for a class of nonlinear dynamic systems with unknown nonlinearities in this paper. The LS-SVM technique is employed to perform approximating unknown nonlinear functions. The updating rule of LS-SVM parameters is derived from Lyapunov stability theory. The proposed control law can guarantee that the output tracking error and the states...
Based on the measured data of hillslope simulated rainfall experiment in the Loess Plateau of China, the method of back-propagation neural networks optimized by genetic algorithms was used to establish the hillslope runoff and infiltration model. The rainfall intensity, rainfall duration, initial soil water content and slope were selected as the model inputs, the runoff volume and infiltration volume...
This paper has combined neural network with fuzzy control and achieved the self-learning and adaptive of fuzzy-controller, has realized the control of washing machines by a fuzzy-neural network optimization algorithms based on Hierarchical Genetic Algorithm (HGA). In addition, fuzzy-neural network structure and weight parameters have been optimized and the optimal control of the washing machine with...
Objective: Discussion based on neural networks in the 31P MR spectroscopy to distinguish hepatocellular carcinoma, normal liver and cirrhosis in value. Methods: Using self-organizing map neural network (SOM) analyse 66 data of 31P MRS, including hepatocellular carcinoma (13 samples), normal liver (16 samples) and liver cirrhosis (37 samples). Results: 31P MRS can be used for the diagnosis and differential...
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...
Logistics service level and logistics cost are in contradiction while they are also in inter-linkage unity from empirical aspect. Through analyzing the game relationship between enterprise logistics service level and logistics cost, the function relationship between logistics service level and logistics cost (sales income) is simulated with the help of the least square method. Basing on these, the...
In this paper, we consider the contingent claim pricing and hedging of European call option. The theory of stock trading volume is applied to describe and study the fluctuations of stock prices in a stock market, and we obtain the formula for pricing a European call option. Then we discuss the range of parameters of the formula in a risk-averse market, and give the corresponding option pricing bounds...
In this paper we study on the prediction technique of network situation awareness. It has two levels: the high-level situation and the low-level next attack step. The first one includes the indexes and the evaluation results of the network security situation, they are figure form, we use the RBF network to predict them for RBFpsilas self-learning character. Then we use the weighted attack graph to...
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