Asymptotic stability analysis in uncertain multi-delayed state neural networks via Lyapunov–Krasovskii theory

This paper presents a new approach to the analysis of asymptotic stability of artificial neural networks (ANN) with multiple time-varying delays subject to polytope-bounded uncertainties. This approach is based on the Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique with the use of a recent Leibniz–Newton model based transformation without including any additional dynamics.Three examples with numerical simulations are used to illustrate the effectiveness of the proposed method. The first example considers the neural network with multiple time-varying delays, which may be seen as a particular case of the second example where it is subject to uncertainties and multiple time-varying delays. Finally, the third example analyzes the stability of the neural network with higher numbers of neurons subject to a single time-delay. The Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability in the bifurcation point.     

Conditions for the emergence of spatially asymmetric retrieval states in an attractor neural network

In this paper we show that during the retrieval process in a binary symmetric Hebb neural network, spatially localized states can be observed when the connectivity of the network is distance-dependent and a constraint on the activity of the network is imposed, which forces different levels of activity in the retrieval and learning states. This asymmetry in the activity during retrieval and learning is found to be a sufficient condition to observe spatially localized retrieval states. The result is confirmed analytically and by simulation.     

Neural networks and organizational systems: Modeling non-linear relationships

For decades, organizational researchers have employed standard statistical methods to uncover relationships among variables and constructs. However, in complex organization systems, the prevalence of non-linearity and outliers is to be expected. Under such circumstances, the use of standard statistical methods becomes unreliable and, correspondingly, results in degraded predictions of the relationships within the organizational systems. We describe the use of neural network analyses to model team effectiveness so as to provide more accurate predictions for managers.     

综合评判的神经网络方法

综合评判的方法已经广泛运用到信息，决策，管理和控制等领域，但是它的两大难点－权重和单因素评价矩阵的确定越来越来明显地展现在人们面前，本文试图用神经网络方法来解决这两个难点，并从理论上证明了这种方法的收敛性。     

Implementing an efficient minimum capacity cut algorithm

In this paper, we present an efficient implementation of theO(mn + n ² logn) time algorithm originally proposed by Nagamochi and Ibaraki (1992) for computing the minimum capacity cut of an undirected network. To enhance computation, various ideas are added so that it can contract as many edges as possible in each iteration. To evaluate the performance of the resulting implementation, we conducted extensive computational experiments, and compared the results with that of Padberg and Rinaldi's algorithm (1990), which is currently known as one of the practically fastest programs for this problem. The results indicate that our program is considerably faster than Padberg and Rinaldi's program, and its running time is not significantly affected by the types of the networks being solved.Corresponding author.     

Resource downgrading

This paper aims to develop models to optimally manage costs associated with resources that can be downgraded. These resources are reused a number of times before becoming unsuitable for their original purpose, and then they are assigned for some other purpose. The typical decisions are the quantity of resources to purchase, to downgrade and to hold in the inventory. A network-based model is developed to formulate the problem and to investigate several special cases. As the model becomes an integer program due to some side constraints, several heuristics are developed here to overcome the challenges associated with solving the resulting integer program. A semiconductor industry application for test wafer management is presented using real-life data.     

Self-averaging in complex brain neuron signals

Nonlinear statistical properties of Ventral Tegmental Area (VTA) of limbic brain are studied in vivo. VTA plays key role in generation of pleasure and in development of psychological drug addiction. It is shown that spiking time-series of the VTA dopaminergic neurons exhibit long-range correlations with self-averaging behavior. This specific VTA phenomenon has no relation to VTA rewarding function. Last result reveals complex role of VTA in limbic brain. Received 17 April 2002 / Received in final form 30 September 2002 Published online 31 December 2002     

Survivor statistics and damage spreading on social network with power-law degree distributions

Damage spreading(DS) of the random graph networks with power-law degree distributions is investigated using Glauber dynamics. Various subgraphs defined by the probability of acquaintance show distinct features in DS as measured by Hamming distance. A heuristic understanding of the long-time value of damage is achieved through an analysis of the survivor statistics. All survivors are dynamical, flipping in unison for the controlled sample and the damaged sample. Verification of these dynamic survivors is achieved through the introduction of a new measure of self-damage.     

度量空间的k－映射像

Ｅ．Ｈａｌｆａｒ（［６］）引入了ｋ－映射概念，本文给出了度量空间ｋ－映像的一个内在刻划，并由此得到一些度量化定理。     

单个函数平移和伸缩的叠加在L~p(R~d)中的逼近(英)

本文主要讨论单个函数平移和伸缩的线性组合对Lp（Rn）中任一紧集内函数的逼近．给出一个很强的逼近结果．这在神经网络应用研究中具有重要意义．