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.     

非线性连续联想记忆神经网络的分析和优化设计

研究非线性连续联想记忆神经网络的渐近稳定性,得出几个定理.在此基础上,提出了一 种优化设计方法,并给出了理论证明.目前已有的若干结论是本文所得定理的特例.     

Decentralized Event-triggered Stability Analysis of Neutral-type BAM Neural Networks with Markovian Jump Parameters and Mixed Time Varying Delays

This paper investigates decentralized event-triggered stability analysis of neutral-type BAM neural networks with Markovian jump parameters and mixed time varying delays. We apply the decentralized event triggered approach to the bidirectional associative memory (BAM) neural networks to reduce the network traffic and the resource of computation. A bidirectional associative memory neural networks is constructed with the mixed time varying delays and Markov process parameters. The criteria for the asymptotically stability are proposed by using with the Lyapunov-Krasovskii functional method, reciprocal convex property and Jensen’s inequality. Stability condition of neutral-type BAM neural networks with Markovian jump parameters and mixed delays is established in terms of linear matrix inequalities. Finally three numerical examples are given to demonstrate the effectiveness of the proposed results     

Hopfield在计算机编程中的应用与研究

Hopfield网络,又称联想记忆网络。文中根据Hopfleld神经网络构造一个应用于计算机代码编程中的联想存储器。联想记忆是该存储器的重要功能,它具有信息记忆和信息联想的特点,能够从不完整的或模糊的信息联想出存储在记忆中的某个完整清晰的信息模式。根据这一原理,用H0pfield联想存储器知识和eclipse插件机制来搭建嵌入在eclipse开发工具中一个知识可拓展的动态帮助插件,实现根据残缺不全的java代码联想到完整的java代码的功能,并进一步阐述Hopfield神经网络在计算机代码编程中的应用前景和发展方向。     

Design of Hopfield network for cryptographic application by spintronic memristors

Memory being one of the essential credential in today’s computer world seeks forward newer research interests in its types. Hopfield neural networks of artificial neural networks are one of its classes that can be modelled to form an associative memory. In this paper, we have shown the Hopfield neural network constructed with spintronic memristor bridges accounting to act as an associative memory unit. The memristors are nanoscaled, in terms of size, which possess synaptic behaviour in the artificial neuromorphic system. The associative behaviour is realised by the updation of synaptic weights of memristive Hopfield with single- and multiple-bit associativity which is simulated in MATLAB. The application of the hardware in the field of cryptography is also proposed.

     

联想记忆与人工神经网络

联想记忆是人类记忆的基本方式,本文通过对人类联想记忆的本质及其规律的分析,讨论了如何用人工神经网络的模型来实现这种记忆形式,同时也指出了这种模拟的不足之处及需要解决的问题。     

New delay-dependent exponential stability criteria of BAM neural networks with time delays

In this paper, the global exponential stability is investigated for the bi-directional associative memory networks with time delays. Several new sufficient conditions are presented to ensure global exponential stability of delayed bi-directional associative memory neural networks based on the Lyapunov functional method as well as linear matrix inequality technique. To the best of our knowledge, few reports about such “linearization” approach to exponential stability analysis for delayed neural network models have been presented in literature. The method, called parameterized first-order model transformation, is used to transform neural networks. The obtained conditions show to be less conservative and restrictive than that reported in the literature. Two numerical simulations are also given to illustrate the efficiency of our result.     

混沌神经网络的研究进展

回顾了近年来几种主要混沌神经元模型及混沌神经网络的研究进展，介绍了其特点及主要的应用。已有的研究结果表明，混沌神经网络在求解复杂优化问题和联想记忆等方面比现有网络有着更好的性能。     

Financial distress prediction using the hybrid associative memory with translation

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.     

A MOS circuit for a nonmonotonic neural network with excellentretrieval capabilities

An MOS circuit is proposed for implementing a nonmonotonic transfer characteristic of a neural network. The present research is motivated by the recent results of theoretical studies showing excellent equilibrium properties of networks with the nonmonotonic neural units. These properties include enhancement of storage capacity and complete elimination of noise in associative memory recall. The simple form of the transfer characteristic enables one to implement it with a simple electrical circuit of standard MOS transistors. SPICE simulation results are shown for the behavior of the neural units in associative memory recall.     

Hopfield网络中二元正交记忆模式的吸引域分析

在作为联想记忆的Hopfield网络中,二元正交记忆模式的分析对网络记忆容量的研究起着重要作用。文中提出了利用吸引指数的概念对各个二元正交记忆模式的吸引域进行估计的方法。理论分析和计算机仿真表明,当网络容量不超过0．33N时（比通常的0．15N要好）,每个二元正交记忆模式的吸引域至少包含一个汉明球。     

时滞杂交双向联想记忆神经网络的全局指数稳定性

在近十几年里，已提出了一类与双向联想记忆相联系的神经网络模型，这些模型推广了单层自联想Hebbian相关器为两层异联想模式匹配器，因而，这类网络在模式识别、信号与图像处理等领域中有广阔的应用前景．研究了带离散时滞杂交双向联想记忆神经网络的收敛特性，利用Halanay型不等式获得了网络全局指数稳定性的充分条件，所得结果是与时滞无关的；已证明利用Halanay型不等式获得的结果改进了由Lyapunov方法获得的结果，而且获得的结果容易判定，并且给出了一个数值例子以说明所得结论的正确性．     

Periodic oscillation of discrete-time bidirectional associative memory neural networks

In this paper, a discrete-time bidirectional associative memory neural networks model is considered. By employing the theory of coincidence degree and using Halanay-type inequality technique we give some sufficient conditions ensuring the existence and globally exponential stability of periodic solutions for the discrete-time bidirectional neural networks. An example with the numerical simulations is provided to show the correctness of our analysis.     

Bifurcation Analysis for Simplified Five-Neuron Bidirectional Associative Memory Neural Networks with Four Delays

The paper deals with the stability and bifurcation analysis of a class of simplified five-neuron bidirectional associative memory neural networks with four     

On the problem of spurious patterns in neural associative memorymodels

The problem of spurious patterns in neural associative memory models is discussed. Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out. A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebbian learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns. With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition.     

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.     

混沌神经网络研究进展与展望

概述了混沌动力学的特性，回顾了近年来混沌神经元主混沌神经网络的研究进展，在此基础上，介绍了两种混沌神经网络模型，分析了其构成和特点，已有研究结果表明，混沌神经网络在联想记忆和组合优化等方面有现有网络更好的性能，最后，指出了混沌神经网络的应用与研究方向。     

Anti-periodic solutions in a ring of four neurons with multiple delays

This paper is concerned with the existence and exponential stability of anti-periodic solutions of bidirectional associative memory (BAM) neural networks with multiple delays. Applying inequality techniques and Lyapunov method, Sufficient conditions which ensure the existence and exponential stability of anti-periodic solutions of the BAM neural networks are presented. Our results are new and supplement some previously known ones.     

Dissipativity analysis of complex-valued BAM neural networks with time delay

Neural Computing and Applications - This paper is concerned with dissipativity analysis of complex-valued bidirectional associative memory (BAM) neural networks (NNs) with time delay. Some novel...     

Some multistability properties of bidirectional associative memory recurrent neural networks with unsaturating piecewise linear transfer functions

Multistability is an important dynamical property in neural networks in order to enable certain applications where monostable networks could be computationally restrictive. This paper studies some multistability properties for a class of bidirectional associative memory recurrent neural networks with unsaturating piecewise linear transfer functions. Based on local inhibition, conditions for globally exponential attractivity are established. These conditions allow coexistence of stable and unstable equilibrium points. By constructing some energy-like functions, complete convergence is studied.