27206002
Exponential Stability Analysis of Neural Networks with Multiple Time Delays
Without assuming the boundedness, strict monotonicity and differentiability of the activation function, a result is established for the global exponential stability of a class of neural networks with multiple time delays. A new sufficient condition guaranteeing the uniqueness and global exponential stability of the equilibrium point is established. The new stability criterion imposes constraints, expressed by a linear matrix inequality, on the self-feedback connection matrix and interconnection matrices independent of the time delays. The stability criterion is compared with some existing results, and it is found to be less conservative than existing ones.
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>>
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.
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.
694856
Support Concurrent Queries in Multiuser CBIR Systems
Various techniques have been developed for different query types in content-based image retrieval (CBIR) systems such as sampling queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate efficient processing techniques for this new framework. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicates that the proposed optimization can significantly reduce average response time in a multiuser environment.
915132
On Randomized Broadcasting and Gossiping in Radio Networks
This paper has two parts. In the first part we give an alternative (and much simpler) proof for the best known lower bound of ?(Dlog (N/D)) time-steps for randomized broadcasting in radio networks with unknown topology. In the second part we give an O(N log3N)- time randomized algorithm for gossiping in such radio networks. This is an improvement over the fastest previously known algorithm that works in time O(N log4 N).
1796961
Dynamically Constructing Classification Rules with Visualization Techniques
7733753
A geometric approach to determine association and coherence of the activation times of cell-cycling genes under differing experimental conditions
Differing arresting agents and protocols can be used to synchronize cells in cultures to specific phases of the cell when studying cell-cycle gene expressions. Often, data derived from individual experiments are analyzed separately, since no appropriate statistical methodology is available at the moment to analyze the data from all such experiments simultaneously. The focus of this paper is to determine the association and coherence of the relative activation times of cell-cycling genes under different experimental conditions. Using a circular-circular regression model, we define two parameters, a rotation parameter for the angular difference between cells' arresting times (phases) in two cell-cycle experiments, and an association parameter to describe the correspondence between the cycle times of maximal expression (phase angles) for a set of genes studied in two experiments. Further, we propose a procedure to assess coherence across multiple experiments, i.e. to what extent the circular ordering of the phase angles of genes is maintained across multiple experiments. Coherence of genes across experiments suggests that functionally these genes tend to respond in a stereotypically sequenced way under different experimental conditions. Our proposed methodology is illustrated by applying it to a HeLa cell-cycle gene-expression data.
18721223
Phase analysis of circadian-related genes in two tissues
BackgroundRecent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4–8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues.ResultsWe propose a set of techniques from circular statistics to analyze phase angles of circadian-related genes in two tissues. We first estimate the phases of a cycling gene separately in each tissue, which are then used to estimate the paired angular difference of the phase angles of the gene in the two tissues. These differences are modeled as a mixture of two von Mises distributions which enables us to cluster genes into two groups; one group having synchronized transcripts with the same phase in the two tissues, the other containing transcripts with a discrepancy in phase between the two tissues. For each cluster of genes we assess the association of phases across the tissue types using circular-circular regression. We also develop a bootstrap methodology based on a circular-circular regression model to evaluate the improvement in fit provided by allowing two components versus a one-component von-Mises model.ConclusionWe applied our proposed methodologies to the circadian-related genes common to heart and liver tissues in Storch et al. [2], and found that an estimated 80% of circadian-related transcripts common to heart and liver tissues were synchronized in phase, and the other 20% of transcripts were lagged about 8 hours in liver relative to heart. The bootstrap p-value for being one cluster is 0.063, which suggests the possibility of two clusters. Our methodologies can be extended to analyze peak expression times of circadian-related genes across more than two tissues, for example, kidney, heart, liver, and the suprachiasmatic nuclei (SCN) of the hypothalamus.