This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

A novel organization of switched capacitor charge pump circuits based on voltage doubler structures is presented in this paper. Each voltage doubler takes a dc input and outputs a doubled dc voltage. By cascading n voltage doublers the output voltage increases up to 2/sup n/ times. A two-phase voltage doubler and a multiphase voltage doubler (MPVD) structures are discussed and design considerations are presented. A simulator working in the Q-V realm was used for simplified circuit level simulation. In order to evaluate the power delivered by a charge pump, a resistive load is attached to the output of the charge pump and an equivalent capacitance is evaluated. A comparison of the voltage doubler circuits with Dickson charge pump and Makowski's voltage multiplier is presented in terms of the area requirements, the voltage gain, and the power level. This paper also identifies optimum loading conditions for different configurations of the charge pumps. Design guidelines for the desired voltage and power levels are discussed. A two-stage MPVD was fabricated using MOSIS 2.0-/spl mu/m CMOS technology. It was designed with internal frequency regulation to reduce power consumption under no load condition.

This paper proposed a novel approach for the Power Quality (PQ) disturbances classification based on the wavelet transform and self organizing learning array (SOLAR) system. Wavelet transform is utilized to extract feature vectors for various PQ disturbances based on the multiresolution analysis (MRA). These feature vectors then are applied to a SOLAR system for training and testing. SOLAR has three advantageous over a typical neural network: data driven learning, local interconnections and entropy based self-organization. Several typical PQ disturbances are taken into consideration in this paper. Comparison research between the proposed method, the support vector machine (SVM) method and existing literature reports show that the proposed method can provide accurate classification results. By the hypothesis test of the averages, it is shown that there is no statistically significant difference in performance of the proposed method for PQ classification when different wavelets are chosen. This means one can choose the wavelet with short wavelet filter length to achieve good classification results as well as small computational cost. Gaussian white noise is considered and the Monte Carlo method is used to simulate the performance of the proposed method in different noise conditions.

In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approximation algorithm avoids overfitting by means of a novel and effective stopping criterion based on the estimation of the signal-to-noise-ratio figure (SNRF). Using SNRF, which checks the goodness-of-fit in the approximation, overfitting can be automatically detected from the training error only without use of a separate validation set. The algorithm has been applied to problems of optimizing the number of hidden neurons in a multilayer perceptron (MLP) and optimizing the number of learning epochs in MLP's backpropagation training using both synthetic and benchmark data sets. The OAA algorithm can also be utilized in the optimization of other parameters of NNs. In addition, it can be applied to the problem of function approximation using any kind of basis functions, or to the problem of learning model selection when overfitting needs to be considered.

Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.

An efficient method to select an optimum set of test points for dictionary techniques in analog fault diagnosis is proposed. This is done by searching for the minimum of the entropy index based on the available test points. First, the two-dimensional integer-coded dictionary is constructed whose entries are measurements associated with faults and test points. The problem of optimum test points selection is, thus, transformed to the selection of the columns that isolate the rows of the dictionary. Then, the likelihood for a column to be chosen based on the size of its ambiguity set is evaluated using the minimum entropy index of test points. Finally, the test point with the minimum entropy index is selected to construct the optimum set of test points. The proposed entropy-based method to select a local minimum set of test points is polynomial bounded in computational cost. The comparison between the proposed method and other reported test points selection methods is carried out by statistical experiments. The results indicate that the proposed method more efficiently and more accurately finds the locally optimum set of test points and is practical for large scale analog systems.