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In this paper, a new method for the control of the shape of the conditional output probability density function (pdf) for general nonlinear dynamic stochastic systems is presented using two-step neural networks (NNs). Following the square-root B-spline NN approximation to the measured output pdf, the problem is transferred into the tracking of dynamic weights. Different from the previous related works, time-delay dynamic NNs with undetermined parameters are employed to identify the non- linear relationships between the control input and the weighting vectors. In order to achieve the required control objective and satisfy the state constraints due to the property of output pdfs, a constrained PI tracking controller is designed by solving a class of linear matrix inequalities and algebraic equations. With the proposed tracking controller and adaptive projection algorithms, both identification and tracking errors can be made to converge to zero, and the state constraints can also be simultaneously guaranteed. Finally, two simulated examples are given, which effectively demonstrate the use of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we continue to explore application of nonsmooth analysis to the study of global asymptotic robust stability (GARS) of delayed neural networks. In combination with Lyapunov theory, our approach gives several new types of sufficient conditions ensuring GARS. A significant common aspect of our results is their low computational complexity. It is demonstrated that the reported results can be verified either by conducting spectral decompositions of symmetric matrices associated with the uncertainty sets of network parameters, or by solving a semidefinite programming problem. Nontrivial examples are constructed to compare with some closely related existing results. [ABSTRACT FROM AUTHOR]

The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system. [ABSTRACT FROM AUTHOR]

In this paper, a biologically inspired, CNN-based, multi-channel, texture boundary detection technique is presented. The proposed approach is similar to human vision system. The algorithm is simple and straightforward such that it can be implemented on the cellular neural networks (CNNs). CNN contains several important advantages, such as efficient real-time processing capability and feasible very large-scale integration (VLSI) implementation. The proposed algorithm also had been widely tested on synthetic texture images. Those texture images are randomly selected from the Brodatz textures database [1]. According to our simulation results, the boundaries of uniform textures can be detected quite successfully. For the nonuniform or nonregular textures, the results also indicate meaningful properties, and the properties also are consistent to the human visual sensation. The proposed algorithm also has been implemented on the CNN Universal Machine (CNN-UM), and yields similar results as the simulation on the PC. Based on the efficient performance of CNN-UM, the algorithm becomes very fast. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals. [ABSTRACT FROM AUTHOR]

In this paper, first, we investigate the CNN templates producing periodic patterns. Next, two image processing algorithms using periodic pattern formation in cellular neural networks are proposed. By applying one to a certain image, area having spatial uniformity is removed from the original image and only parts without spatial uniformity remain. Simulation results show that when this algorithm is applied to an image containing characters (texts) or a human face image, only characters or eyes and mouth are extracted. Also, by applying the other to texture image, periodicity is enhanced in area having spatial periodicity, and texture can be segmented. Simulation results show that when this algorithm is applied to image that consists of four kinds of textures, each texture area can be segmented. [ABSTRACT FROM AUTHOR]

This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choke of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity. [ABSTRACT FROM AUTHOR]

In this paper, an on-chip implementation of the active contour technique called pixel-level snakes is proposed. This is based on an optimized cellular neural network (CNN) algorithm with capabilities to support changes in the contour topology. The entire algorithm has been implemented on a 64 × 64 CNN universal machine chip-set architecture for which the results of the time performance measurements are given. To illustrate the validity and capabilities of the proposed implementation some on-chip experiments are also included. [ABSTRACT FROM AUTHOR]

The promising potential of cellular neural networks (CNN) has resulted in the development of several template design methods. The CNN universal machine (CNN-UM), a programmable CNN, has made it possible to create image-processing algorithms that run on this platform. However, very large-scale integration implementations of CNN-UMs presented parameter deviations that do not occur on ideal CNN structures. Consequently, new design methods were developed aiming at more robust templates. Although these new templates were indeed more robust, erroneous behavior can still be observed. An alternative for chip-independent robustness is chip-specific optimization, where the template is targeted to an individual chip. This paper describes a solution proposal in this sense to automatically tune templates in order to make the chip react as an ideal CNN structure. The approach uses measurements of actual CNN-UM chips as part of the cost function for a global optimization method to find an optimal template given an initial approximation. Further improvements are achieved by generating chip-specific robust templates by doing a search for the best template among the optimal ones. The tuned templates are therefore customized versions that are expected to be much less sensitive to imperfections on the operation of CNN-UM chips. Results are presented for the binary and grayscale cases, including the case of grayscale output. It is expected that as this technique matures, it will give CNN-UM chips enough reliability to compete with digital systems in terms of robustness in addition to advantages of speed. [ABSTRACT FROM AUTHOR]

A new result is developed for separating nonlinearly mixed signals in which the nonlinearity is characterized by a class of strictly monotonic continuously differentiable functions. The structure of the blind inverse system is explicitly derived within the framework of maximum likelihood estimation and the system culminates to a special architecture of the 3-layer perceptron neural network where the parameters in the first layer are inversely related to the output layer. The proposed approach exploits both the structural and signal constraints to search for the solution and assumes that the cumulants of the source signals are known a priori. A novel statistical algorithm based on the hybridization of the generalized gradient algorithm and metropolis algorithm has been derived for training the proposed perceptron which results in improved performance in terms of accuracy and convergence speed. Simulations and real-life experiment have also been conducted to verify the efficacy of the proposed scheme in separating the nonlinearly mixed signals. [ABSTRACT FROM AUTHOR]