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.
38675279
On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method
This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance.
31887438
Robust Stability Analysis of a Class of Hopfield Neural Networks with Multiple Delays
The robust stability of a class of Hopfield neural networks with multiple delays is analyzed. Sufficient conditions for the global robust stability of the equilibrium point are established through constructing a suitable Lyapunov-Krasovskii functional. The present results take the form of linear matrix inequalities, and are computationally efficient. In addition, the results are independent of delays and established without assuming differentiability and monotonicity of the activation function.
31045346
A New Fuzzy Identification Method Based on Adaptive Critic Designs
A new fuzzy identification method for unknown nonlinear discrete systems is presented by introducing adaptive critic designs into the generalized fuzzy hyperbolic model (GFHM). This method minimizes the long-time error other than the immediate error to improve the identification effect. We first represent the GFHM with a neural network structure, and then utilize the adaptive critic designs (ACDs) to get the optimal parameters of the network so that the long-time identification error is minimized. Finally, we give a simulation example to verify the effectiveness of this identification method.
38948507
Analysis and synthesis of a class of neural networks with sparse interconnections
The authors present results for the analysis and synthesis of a class of neural networks with sparse or partial interconnecting structure. The, design procedure guarantees to synthesize neural networks with arbitrarily predetermined sparse interconnection structures and to store any given set of desired bipolar patterns as memories. It is shown that a sufficient condition for the existence of a design for neural networks with sparse interconnection is self-feedback for every neuron in the network. Several specific examples are included to demonstrate the applicability of the methodology.<<ETX>>
30709401
T-graphs and the channel assignment problem
Abstract T -colorings arose from the channel assignment problem in communications. Given a finite set T of non-negative integers, a T -coloring of a simple graph G is an assignment of a non-negative integer (channel) on every vertex of G , such that the difference of channels of two adjacent vertices does not fall in T . The T -span of G , denoted by sp T ( G ), is the minimum span among all possible T -colorings of G , where the span of a T -coloring is the difference between the largest and smallest channels used. This article applies T -graphs to explore the sets T that belong to these two collections: G = { T : greedy (or first-fit) algorithm provides T -spans for all complete graphs}, and E = { T : spt ( G ) = sp T ( K χ ( G ) ) for all graphs G , where χ is the chromatic number}. Given T , its T -graph has the vertex set of all non-negative integers so that two vertices are adjacent if their difference does not fall in T . We show that for any a ∈ Z + , the T -graph of aT is isomorphic to a disjoint product of a copies of the T -graph of T , where aT is the set obtained by multiplying every element of T by a . Based on this characterization, the following two results are attained. For any a ∈ Z + , T ∈ E if and only if aT ∈ E , and T ∈ G if and only if aT ∈ G . The second fact has been proven by Cozzens and Roberts (1991) from a different approach. We will completely solve the family of sets T = {0, s , s + 1,…, l } by providing a different proof of the fact T ∈ G (Tesman, 1989), and showing that T ∈ E if and only if l is a multiple of s . In addition, complete solutions for a more general family are obtained: for any a , s , l ∈ Z + with s ⩽ l , T = {0, as , a ( s + 1), a ( s + 2),…, al } ∪ A ∈ E , and T ϵ E if and only if l = ms for some m , where A ⊆{ as + 1, as + 2, …, al - 1}.
237907
IC power distribution challenges
With each technology generation, delivering a time-varying current with reduced nominal supply voltage variation is becoming more difficult due to increasing current and power requirements. The power delivery network design becomes much more complex and requires accurate analysis and optimizations at all levels of abstraction in order to meet the specifications. We describe techniques for estimation of the supply voltage variations that can be used in the design of the power delivery network. We also describe the decoupling capacitor hierarchy that provides a low impedance to the increasing high-frequency current demand and limits the supply voltage variations. Techniques for high-level power estimation that can be used for performance vs. power trade-offs to reduce the current and power requirements of the circuit are also presented.
1870907
Macro-modeling concepts for the chip electrical interface
The power delivery network is made up of passive elements in the distribution network, as well as the active transistor loads. A chip typically has three types of power supplies that require attention: core, I/O, and analog. Core circuits consist of digital circuits and have the largest current demand. In addition to all of the system issues/models for the core, modeling the I/O subsystem has the additional requirement of modeling return paths and discontinuities. The analog circuits present yet a different challenge to the macro-modeling of the supply network because they place a tight demand on supply variations. This paper presents a design methodology on how to generate macro-models of the entire chip electrical interface. This methodology can be used by the chip, package, and system designers and is being used to design high-reliability servers.
28065086
T-Colorings and Chromatic Number of Distance Graphs
163750
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today’s Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.