基于约束区域的连续时间联想记忆神经网络

传统的联想记忆神经网络模型是根据联想记忆点设计权值。文中提出一种根据联想记忆点设计基于约束区域的神经网络模型,它保证了渐近稳定的平衡点集与样要点集相同,不渐近稳定的平衡点恰为实际的拒识状态,并且吸引域分布合理。它具有学习和遗忘能力,还具有记忆容量大和电路可实现优点,是理想的联想记忆器。     

基于线性规划的联想记忆神经网络模型

提出一种基于优化线性函数的神经网络联想记忆器,打破了将待识别模式作为网络起始点的常规,它能保证渐近稳定的平衡点集与样本点集相同,吸引域分布合理,不渐近稳定的平衡点恰为实际的拒识模式,并且电路实现容易,对拒识模式有清楚的解释。理论分析和计算机模拟都表明本文的模型是理想的联想记忆器,还可降低对硬件的精度要求。     

离散Hopfield联想记忆神经网络的渐近行为

该文对一类离散Hopfield联想记忆神经网络的渐近行为进行了讨论，首先提出这类I／O函数取为Sigmoid型函数的离散Hopfield联想记忆神经网络的数学模型，讨论并给出了这种模型的一系列性质，例如运动轨迹的有界性，平衡点的唯一性以及渐近稳定性等，得到了平衡点渐近稳定的充分条件，检验这种神经网络模型的渐近稳定性，只需要测试一个特定矩阵的定性性质或特定不等式即可，这些结果可用于离散Hopfield联想记忆神经网络的综合过程。     

离散Hopfield双向联想记忆神经网络的稳定性分析

首先将离散Hopfield双向联想记忆神经网络转化成一个特殊的离散Hopfield网络 模型.在此基础上,对离散Hopfield双向联想记忆神经网络的全局渐近稳定性和全局指数稳 定性进行了新的分析.证明了神经网络连接权矩阵在给定的约束条件下有唯一的而且是渐近 稳定的平衡点.利用Lyapunov方程正对角解的存在性得到了几个判定平衡点为全局渐近稳 定和全局指数稳定的充分条件.这些条件可以用于设计全局渐近稳定和全局指数稳定的神经 网络.所做的分析扩展了以前的稳定性结果.     

噪声环境中时滞双向联想记忆神经网络指数稳定

任何系统实际上都是在噪声环境中进行工作的.对处在噪声强度已知的噪声环境下双向联想记忆(BAM)神经网络,其平衡点具有指数渐近稳定性是网络进行异联想记忆的基础.构造一个适当的Lyapunov函数,应用It^o公式、M矩阵等工具讨论了在噪声环境下具有时滞的BAM神经网络概率1指数渐近稳定,得到了指数稳定的代数判据和两个推论,此判据只需验证仅由网络参数构成的矩阵是M矩阵即可,给网络设计带来方便.本文所得结果包括相关文献中确定性结果作为特例.     

中立联想记忆神经网络鲁棒稳定性新准则

针对一类具有离散时滞和参数范数有界的不确定性中立联想记忆神经网络的全局渐近鲁棒稳定性问题进行了研究.通过应用范数理论和矩阵不等式分析方法,并构造合适的Lyapunov-Krasovskii泛函,推导出了与时滞无关的新稳定性判定准则,用于保证神经网络的平衡点是全局渐近鲁棒稳定的.该准则中包含的未知参数少、计算复杂度低,易于验证.仿真算例验证了新判定准则的有效性.     

联想记忆神经网络局部指数稳定的充要条件及特征函数

讨论非线性连续联想记忆神经网络平衡点局部指数稳定的判定条件及平衡点指数 吸引域的估计,得到了平衡点局部指数稳定的充要条件,并引入一个特征函数,可以判定平衡 点的邻域是否为指数吸引域.文中给出一族范数下(所有单调范数)网络局部或全局指数稳定 的判定条件,推广了已知文献在特定范数下所得到的结论.     

含时延的联想记忆神经网络的指数吸引域和指数收敛速度

采用不等式技巧和非负矩阵性质, 给出了含时延的联想记忆神经网络平衡点的指数吸引域和指数收敛速度估计以及指数稳定的一些判断条件.     

测试维护总线(TM-BUS)的研究与实现

测试维护总线是机载计算机测试技术研究的难点和关键技术之一。简要介绍了测试维护总线ＴＭ－ＢＵＳ控制器的设计,重点论述了ＴＭ－ＢＵＳ的实现方案、ＴＭ－ＢＵＳ控制管理机制及ＴＭ－ＢＵＳ应用。应用系统稳定运行表明,所提出的ＴＭ－ＢＵＳ实现方案的正确性以及芯片逻辑功能正确,稳定可靠。     

一类时延反馈神经网络的稳定性及吸引域的估计

反馈型神经网络由于具有极为丰富的动力学行为和整体计算能力（如优化、联想、振荡和混饨）而倍受关注，近几年的研究表明，当网络的时延足够小时，具有延时的对称Hopfield型神经网络和无时延情况一样也是全局稳定的．本文通过构造适当Lyapunov泛函的方法，对一类具有时延的反馈型神经网络平衡点的渐近稳定性进行了分析，得到了平衡点渐近稳定的充分条件：要检验一个有时间延迟的反馈型神经网络的稳定性，只要测试一个特定矩阵的定性性质或一个特定不等式即可．最后我们也提供了一种估计网络渐近稳定平衡点吸引域的方法．     

A novel continuous-time neural network for realizing associativememory

A novel neural network is proposed in this paper for realizing associative memory. The main advantage of the neural network is that each prototype pattern is stored if and only if as an asymptotically stable equilibrium point. Furthermore, the basin of attraction of each desired memory pattern is distributed reasonably (in the Hamming distance sense), and an equilibrium point that is not asymptotically stable is really the state that cannot be recognized. The proposed network also has a high storage as well as the capability of learning and forgetting, and all its components can be implemented. The network considered is a very simple linear system with a projection on a closed convex set spanned by the prototype patterns. The advanced performance of the proposed network is demonstrated by means of simulation of a numerical example.     

Associative Memory Design via Path Embedding into a Graph

A graph theoretical procedure for storing a set of n-dimensional binary vectors as asymptotically stable equilibrium points of a discrete Hopfield neural network is presented. The method gives an auto-associative memory which stores an arbitrary memory set completely. Spurious memories might occur only in a small neighborhood of the original memory vectors, so cause small errors.     

Synthesis of Brain-State-in-a-Box (BSB) based associative memories

Presents a novel synthesis procedure to realize an associative memory using the Generalized-Brain-State-in-a-Box (GBSB) neural model. The implementation yields an interconnection structure that guarantees that the desired memory patterns are stored as asymptotically stable equilibrium points and that possesses very few spurious states. Furthermore, the interconnection structure is in general non-symmetric. Simulation examples are given to illustrate the effectiveness of the proposed synthesis method. The results obtained for the GBSB model are successfully applied to other neural network models.     

A competitive associative memory model and its dynamics

Conventional associative memory networks perform "noncompetitive recognition" or "competitive recognition in distance". In this paper a "competitive recognition" associative memory model is introduced which simulates the competitive persistence of biological species. Unlike most of the conventional networks, the proposed model takes only the prototype patterns as its equilibrium points, so that the spurious points are effectively excluded. Furthermore, it is shown that, as the competitive parameters vary, the network has a unique stable equilibrium point corresponding to the winner competitive parameter and, in this case, the unique stable equilibrium state can be recalled from any initial key.     

On the synthesis of brain-state-in-a-box neural models with application to associative memory

In this article we present techniques for designing associative memories to be implemented by a class of synchronous discrete-time neural networks based on a generalization of the brain-state-in-a-box neural model. First, we address the local qualitative properties and global qualitative aspects of the class of neural networks considered. Our approach to the stability analysis of the equilibrium points of the network gives insight into the extent of the domain of attraction for the patterns to be stored as asymptotically stable equilibrium points and is useful in the analysis of the retrieval performance of the network and also for design purposes. By making use of the analysis results as constraints, the design for associative memory is performed by solving a constraint optimization problem whereby each of the stored patterns is guaranteed a substantial domain of attraction. The performance of the designed network is illustrated by means of three specific examples.     

An analog feedback associative memory

A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is developed for the Hopfield continuous-time network. An important requirement is that each memory vector has to be an asymptotically stable (i.e. attractive) equilibrium of the network. Some of the limitations imposed by the continuous Hopfield model on the set of vectors that can be stored are pointed out. These limitations can be relieved by choosing a network containing visible as well as hidden units. An architecture consisting of several hidden layers and a visible layer, connected in a circular fashion, is considered. It is proved that the two-layer case is guaranteed to store any number of given analog vectors provided their number does not exceed 1 + the number of neurons in the hidden layer. A learning algorithm that correctly adjusts the locations of the equilibria and guarantees their asymptotic stability is developed. Simulation results confirm the effectiveness of the approach.     

A new feedback neural network with supervised learning

A model is introduced for continuous-time dynamic feedback neural networks with supervised learning ability. Modifications are introduced to conventional models to guarantee precisely that a given desired vector, and its negative, are indeed stored in the network as asymptotically stable equilibrium points. The modifications entail that the output signal of a neuron is multiplied by the square of its associated weight to supply the signal to an input of another neuron. A simulation of the complete dynamics is then presented for a prototype one neuron with self-feedback and supervised learning; the simulation illustrates the (supervised) learning capability of the network.     

An optimization approach to design of generalized BSB neural associative memories

This article is concerned with the synthesis of the optimally performing GBSB (generalized brain-state-in-a-box) neural associative memory given a set of desired binary patterns to be stored as asymptotically stable equilibrium points. Based on some known qualitative properties and newly observed fundamental properties of the GBSB model, the synthesis problem is formulated as a constrained optimization problem. Next, we convert this problem into a quasi-convex optimization problem called GEVP (generalized eigenvalue problem). This conversion is particularly useful in practice, because GEVPs can be efficiently solved by recently developed interior point methods. Design examples are given to illustrate the proposed approach and to compare with existing synthesis methods.