2391589
Synthesis approach for bidirectional associative memories based on the perceptron training algorithm
Abstract Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results, a specific example is considered.
215202
Neural network-based model reference adaptive control system
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.
35821065
Criteria for asymptotic stability of a class of discrete systems with multiple independent variables
This paper investigates the problem of asymptotic stability for a class of linear shift-invariant discrete systems with multiple independent variables. We first establish the equivalence of this problem and the problem of robust stability for a class of ordinary linear shift-varying discrete systems with the matrix uncertainty set defined by the coefficient matrices of the original system. On the basis of this equivalence, by using the variational method and the Lyapunov Second Method, necessary and sufficient conditions for asymptotic stability are obtained in different forms for the class of systems considered in the present paper. The parametric classes of Lyapunov functions which define the necessary and sufficient conditions of asymptotic stability are determined. We use the piecewise linear polyhedral Lyapunov functions of the infinity vector norm type to derive an algebraic criterion for asymptotic stability of the given class of discrete systems in the form of solvability conditions of a set of matrix equations.
7951709
Design of bidirectional associative memories based on the perceptron training technique
Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results a specific example is considered.
8564766
Location-based pairwise key establishments for static sensor networks
Sensor networks are ideal candidates for applications such as target tracking and environment monitoring. Security in sensor networks is critical when there are potential adversaries. Establishment of pairwise keys is a fundamental security service, which forms the basis of other security services such as authentication and encryption. However, establishing pairwise keys in sensor networks is not a trivial task, particularly due to the resource constraints on sensors. This paper presents several techniques for establishing pairwise keys in static sensor networks. These techniques take advantage of the observation that in static sensor networks, although it is difficult to precisely pinpoint sensors' positions, it is often possible to approximately determine their locations. This paper presents a simple location-aware deployment model, and develops two pairwise key predistribution schemes, a closest pairwise keys predistribution scheme and a location-based pairwise keys scheme using bivariate polynomials, by taking advantage of sensors' expected locations. The analysis in this paper indicates that these schemes can achieve better performance if such location information is available and that the smaller the deployment error (i.e., the difference between a sensor's actual location and its expected location) is, the better performance they can achieve.
12777678
Unsupervised Image Layout Extraction
We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabeled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspect-feature dependency from multiple images is learned via the expectation-maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using probabilistic latent semantic analysis.
10993158
Efficient and Distributed Access Control for Sensor Networks
Sensor networks are often used to sense the physical world and provide observations for various uses. In hostile environments, it is critical to control the network access to ensure the integrity, availability, and at times confidentiality of the sensor data. This paper develops efficient methods for distributed access control in sensor networks. The paper starts with a baseline approach, which provides a more flexible and efficient way to enforce access control when compared with previous solutions. This paper then extends the baseline approach to enable privilege delegation, which allows a user to delegate its privilege to other users without using a trusted server, and broadcast query, which allows a user to access the network at a large scale efficiently. The privilege delegation and broadcast query are very useful in practice; none of the current solutions can achieve these two properties.