摘要：Passive filtering of neural networks with time-invariant delay and quantized output is considered.A criterion on the passivity of a filtering error system is proposed by means of the Lyapunov-Krasovskii functional and the Bessel-Legendre *** on the criterion,a design approach for desired passive filters is developed in terms of the feasible solution of a set of linear matrix ***,analyses and syntheses are extended to the time-variant delay situation using the reciprocally convex combination ***,a numerical example with simulations is used to illustrate the applicability and reduced conservatism of the present passive filter design approaches.

摘要：Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

摘要：Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts for ward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.

摘要：Topology design of artificial neural networks （ANNs） is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon （MWOBS） method, to optimize neural network topologies. The advantages and disadvantages of the OBS and unit-OBS were analyzed to develop the method. Actually, optimized topologies are difficult to get within reasonable times for complex problems. Motivating by the mechanism of natural neurons, the MW-OBS method balances the accuracy and the time complexity to achieve better neural network performance. The method will delete multiple connections among neurons according to the second derivative of the error function, so the arithmetic converges rapidly while the accuracy of the neural network remains high. The stability and generalization ability of the method are illustrated in a Java program. The results show that the MWOBS method has the same accuracy as OBS, but time is similar to that of unit-OBS. Therefore, the MWOBS method can be used to efficiently optimize structures of neural networks for large scale applications.

摘要：It is generally impossible to obtain the analytic optimal guidance law for complex nonlinear guidance systems of homing missiles,and the open loop optimal guidance law is often obtained by numerical methods,which can not be used directly in *** neural networks are trained off line using the optimal trajectory of the missile produced by the numerical open loop optimal guidance law,and then,the converged neural networks are used on line as the feedback optimal guidance law in real *** research shows that different selections of the neural networks inputs,such as the system state variables or the rate of LOS(line of sight),may have great effect on the performances of the guidance systems for homing *** robustness for several guidance laws is investigated by simulations,and the modular neural networks architectures are used to increase the approximating and generalizing abilities in the large state *** useful conclusions are obtained by simulation results.

摘要：Fault diagnosis is confronted with two problems; how to '' measure'' the growthof a fault and how to predict the remaining useful lifetime of such a failing component or *** paper attempts to solve these two problems by proposing a model of fault prognosis usingwavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames areused as scaling functions and wavelets, respectively. The centers of the basis functions arecalculated using a dyadic expansion scheme and a k-means clustering algorithm.

摘要：The roll motions of ships advancing in heavy seas have severe impacts on the safety of crews,vessels,and cargoes;thus,it must be *** study presents the design of a rudder roll damping autopilot by utilizing the dual extended Kalman filter(DEKF)trained radial basis function neural networks(RBFNN)for the surface *** autopilot system constitutes the roll reduction controller and the yaw motion controller implemented in *** analyzing the advantages of the DEKF-trained RBFNN control method theoretically,the ship’s nonlinear model with environmental disturbances was employed to verify the performance of the proposed stabilization *** sailing scenarios were conducted to investigate the motion responses of the ship in *** results demonstrate that the DEKF RBFNN based control system is efficient and practical in reducing roll motions and following the path for the ship sailing in waves only through rudder actions.

摘要：The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

摘要：In this paper, we propose the Hyper Basis Function （HBF） neural network on the basis of Radial Basis Function （RBF） neural network. Compared with RBF, HBF neural networks have a more generalized ability with different activation functions. A decision tree algorithm is used to determine the network center. Subsequently, we design an adaptive observer based on HBF neural networks and propose a fault detection and diagnosis method based on the observer for the nonlinear modeling ability of the neural network. Finally, we apply this method to nonlinear systems. The sensitivity and stability of the observer for the failure of the nonlinear systems are proved by simulation, which is beneficial for real-time online fault detection and diagnosis.

摘要：The inverse design of electron lens is realized by two different methods in this paper. One is damped least square method and the other is the artificial neural network method. Their merits and defects are discussed according to our calculation results in the paper. In the condition of selecting the learning samples properly, the artificial neural network has obvious advantages in the inverse design of electron lens. It is an effective method to solve the inverse design problem in the electron optic system.