2568010
Stability of nontrivial solution of delay differential equations with state-dependent impulses
This paper considers the stability problem of nontrivial solutions of delay differential equations with state-dependent impulses. By using the concepts of reduced system and quasi-stability, the stability problem of a nontrivial solution of the impulsive delay differential equation with state-dependent impulses is converted to that of the trivial solution of the impulsive delay differential equation with fixed impulses. Several stability criteria are then established utilizing the known results for systems with fixed impulses.
23517581
Global exponential stability of high order Hopfield type neural networks
This paper studies the problem of global exponential stability for a class of high order Hopfield type neural networks. By utilizing Lyapunov functions, several sufficient conditions for the global exponential stability of equilibrium point are obtained. The exponential convergence rate of the equilibrium point is also estimated. As a special case, several new criteria on global exponential stability for the corresponding first order Hopfield neural networks are obtained. Those criteria generalize some of the recent results reported in the literature.
11705555
RIP: an efficient hybrid repeater insertion scheme for low power
The paper presents a novel repeater insertion algorithm for interconnect power minimization. The novelty of our approach is in the judicious integration of an analytical solver and a dynamic programming based method. Specifically, the analytical solver chooses a concise repeater library and a small set of repeater location candidates such that the dynamic programming algorithm can be performed fast with little degradation of the solution quality. In comparison with previously reported repeater insertion schemes, within comparable runtimes, our approach achieves up to 37% higher power savings. Moreover, for the same design quality, our scheme attains a speedup of two orders of magnitude.
45375926
A Markov chain sequence generator for power macromodeling
In this paper, we present a novel sequence generator based on a Markov chain (MC) model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl+n/sup 2/) time. Derived from a strongly mixing MC, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2000 48-bit vectors are generated in less than 0.05 s, with average deviations of the signal statistics p,d, and s equal to 1.6%, 1.8%, and 2.8%, respectively. Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS'85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p,d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.
44693659
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays
This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.
30635259
On global exponential stability of generalized stochastic neural networks with mixed time-delays
Abstract This paper is concerned with the global exponential stability analysis problem for a general class of stochastic neural networks with mixed time-delays. The mixed time-delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed stochastic neural network is exponentially stable in the mean square in the presence of both the discrete and distributed delays. By employing a new Lyapunov–Krasovskii functional and conducting stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria of the exponential stability. Furthermore, the main results are specialized to deal with the analysis problem for the global asymptotic stability within the same LMI framework. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.
2696332
A boosting approach for motif modeling using ChIP-chip data
MOTIVATION
Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation.


RESULTS
This paper describes a boosting approach to modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight matrix based classifiers, thus yielding a non-linear binding decision rule. The proposed approach was applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared with the weight matrix method, our new approach showed significant improvements on the specificity in a majority of cases.