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。     

基于约束区域的BSB联想记忆模型

提出一种根据联想记忆点设计基于约束区域的ＢＳＢ（Ｂｒａｉｎ－Ｓｔａｔｅ－ｉｎｍ－ａ－Ｂｏｘ）神经网络模型,它保证了渐近稳定的平衡点集与样本点集相同,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,从而将ＥＳＢ完善为理想的联想记忆器。     

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.     

不确定时变时滞组合系统的鲁棒分散控制

研究一类互联项与孤立子系统均含有时变状态时滞的不确定组合系统的状态反馈 鲁棒分散控制问题,利用线性矩阵不等式（LMI）方法设计出线性无记忆状态反馈分散控制 器使受控系统在平衡点处渐近稳定．最后给出的数值例子验证了所给结果的有效性．     

模糊联想记忆网络的模式摄动影响研究

本文利用基于三角模的联想记忆网络的性质以及模糊联想记忆网络的全局鲁棒性定义,研究了训练模式集的摄动对模糊联想记忆网络的影响,并对基于几种算子的模糊联想记忆网络在训练模式存在摄动的情况下的受影响程度进行了比较。     

一个新的高阶双向联想记忆模型及其性能估计

提出了一个新的高阶双向联想记忆模型.它推广了由Tai及Jeng所提出的高阶双向联想记忆模型HOBAM(higher-order bidirectional associative memory)及修正的具有内连接的双向联想记忆模型MIBAM(modified intraconnected bidirectional associative memory),通过定义能量函数,证明了新模型在同步与异步更新方式下的稳定性,从而能够保证所有被训练模式对成为该模型的渐近稳定点.借助统计分析原理,估计了所提模型的存储容量.计算机模拟证实此模型不仅具有较高的存储容量,而且还具有较好的纠错能力.     

模糊联想记忆网络的全局鲁棒性研究——基于爱因斯坦t-模

利用三角模的模糊联想记忆网络的性质以及模糊联想记忆网络的鲁棒性定义,对基于爱因斯坦t-模构建的模糊双向联想记忆网络的学习算法的全局鲁棒性进行了分析。从理论上证明了当训练模式的摄动为正向摄动时,该学习算法可以保持良好的鲁棒性,并用实验验证了该结论;而当摄动存在负向波动时该学习算法不满足全局鲁棒性。然后又进一步对训练模式集摄动最大摄动与输出模式集的最大摄动之间的关系进行研究,得出了训练模式集的最大摄动与输出模式集的最大摄动之间的关系曲线。     

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.     

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.     

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.     

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.     

Specification and implementation of a digital Hopfield-typeassociative memory with on-chip training

The definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology is addressed. Various learning rules that can be integrated in silicon and the associative memory properties of the resulting networks are investigated. The relationships between the architecture of the circuit and the learning rule are studied in order to minimize the extra circuitry required for the implementation of training. A 64-neuron associative memory with on-chip training has been manufactured, and its future extensions are outlined. Beyond the application to the specific circuit described, the general methodology for determining the accuracy requirements can be applied to other circuits and to other autoassociative memory architectures.     

A new paradigm for real-time parallel storage and recognition of patterns based on a hierarchical organization of associative memories utilizing Walsh function encoding

A new hierarchical Walsh memory which can store and quickly recognize any number of patterns is proposed. A Walsh function based associative memory was found to be capable of storing and recognizing patterns in parallel via purely a software algorithmic technique (namely, without resorting to parallel hardware) while the memory itself only takes a single pattern space of computer memory, due to the Walsh encoding of each pattern. This type of distributed associative memory lends itself to high speed pattern recognition and has been reported earlier in a single memory version. In this paper, the single memory concept has first been extended to a parallel memory module and then to a tree-shaped hierarchy of these parallel modules that are capable of storing and recognizing any number of patterns for practical large scale data applications exemplified by image and speech recognition. The memory hierarchy was built by successively applying k-means clustering to the training data set. In the proposed architecture, the clustered data subsets are stored respectively into a parallel memory module where the module allocation is optimized using the genetic algorithm to realize a minimal implementation of the memory structure. The system can recognize all the training patterns with 100% accuracy and further, can also generalize on similar data. In order to demonstrate its efficacy with large scale real world data, we stored and recognized over 500 faces while at same time, achieving much reduced recognition time and storage space than template matching.     

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.