In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can’t maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits.

A processing method of vehicle WIM signals is researched in this paper. According to the characteristics of vehicle weigh-in-motion signals, a self-adaptive filtering variable step size LMS algorithm based on neural network is proposed to replace the traditional self-adaptive filtering method. This method can filter out the noise in each band of WIM signal. In different circumstances, for different vehicle types, it has good adaptability, high accuracy and high speed. Based on this method of signal processing, a vehicle WIM system based on Hopfield Neural Network adaptive filter is developed, a high-performance chip of TMS32C2812 is selected to design a high-performance system. This system can measure the weight of vehicle on highway accurately and timely.

This paper discusses the multi-player non-cooperative game of nonlinear stochastic time-varying systems described by Itô-type differential equations in a finite time interval. Multi-player non-cooperative game problems are represented by multi-objective Pareto (MOP) control problems to describe the fact that each player has their own goals. By applying Hamilton–Jacobi inequalities (HJIs), the criterion of upper bounds of the MOP boundary is obtained for nonlinear stochastic systems, and the corresponding strategies are designed for such games, so the MOP problem is transformed into a HJI-constrained MOP problem. In order to overcome the difficulty of solving HJIs, a global linearization method is proposed to approximate the nonlinear systems. By the proposed global linearization method, multi-player non-cooperative game problems are transformed into Riccati equation-constrained MOP problems, and the approximate solutions of HJI-constrained MOP problems are obtained. Finally, a practical example is given to illustrate the effectiveness of the proposed method.

This paper discusses the state feedback H_∞ control problem for a class of bilinear stochastic systems driven by both Brownian motion and Poisson jumps. By completing square method, we obtain the H_∞ control by solutions of the corresponding Hamilton‐Jacobi equations (HJE). By the tensor power series method, we also shift such HJEs into a kind of Riccati equations, and the H_∞ control is represented with the form of tensor power series.

This study discusses the calculation of entropy of discrete-time stochastic biological systems. First, measurement methods of the system entropy of discrete-time linear stochastic networks are introduced. The system entropy is found to be characterized by system matrices of the discrete-time biological systems. Secondly, the system entropy of nonlinear discrete-time stochastic biological systems is discussed and is calculated based on a global linearization method. The approximation of the values of system entropy of nonlinear stochastic systems needs to solve an optimization problem that is constrained by a kind of linear matrix inequality (LMI). Finally, a practical biochemical system is provided to verify the effectiveness of the proposed calculation method.