一个改进的指数双向联想存储器及性能分析

通过分析Wang的修正指数双向联想存储器(MeBAM),本文提出了一个新的指数式双向联想存储器.该存储器不仅保持了MeBAM的优点,如放宽了Kosko对BAM的连续性假定的限制,排除了BAM的补码问题,同时还大大改善了现有BAM的存储性能和纠错能力.通过定义一个随状态变化而减少的能量函数严格证明了改进的eBAM(IeBAM)在同步与异步方式下的稳定,从而保证了所有要存的模式对成为其稳定点.此外,借助信噪比分析方法给出了IeBAM和MeBAM的信噪比估计.理论分析和计算机模拟结果证实了IeBAM的性能确实优于MeBAM和eBAM.     

多重加权多值指数双向联想记忆网络及其表决性能

Wang和陈等利用各自提出的二值指数双向联想记忆模型（eBAM)及其改进型eBAM(IeBAM),分别构造了由多个eBAM和IeBAM组成的多重eBAM(Multi-eBAM)和多重IeBAM(Multi-IeBAM)的信念组合模型,使之可模拟多个专家的表决。该文在此基础上,借助陈提出的多值eBAM(MVeBAM),提出了多重多值eBAM (Multi-MVeBAM),对Multi-eBAM和Multi-IeBAM进行了两方面的推广;一是将二值表示推广到多值表示,以此可以处理现实中的多值数据;二是将原有模型中具有同等权威度的各专家推广到各具不同的权威度的专家,以此模拟更实际的表决情形。文中借助能量函数证明了所提模型的渐近稳定性,以保证其实际可用。计算机模拟证实了模型的可行性。     

改进的指数双向联想记忆模型在数据压缩中的应用

改进的指数双向联想记忆模型（Ｉmproved exponential bidirectional associative memory molel,IeBAM)是在eBAM的基础上通过引入内连接项而产生的一个比eBAM具有更高存储容量,纠错性的联想神网络,借助于ＩeBAM的高存储容量和良好的纠错性能及有序直方,要实现一种更高效率的数据压缩方法,从而为现有的数据压缩方法提供一种新算法,最后,计算机模拟证实了使用ＩeBAM的数据压缩性能比使用eBAM的更好。     

一类离散BAM神经网络的全局渐近稳定性分析--LMI方法

提出一种新的神经网络模型--标准神经网络模型(SNNM),并给出基于线性矩阵不等式(LMI)的SNNM平衡点的全局渐近稳定性定理.通过状态的线性变换,将推广的离散BAM神经网络转化为SNNM,利用SNNM的稳定性结论,判定该离散BAM的全局渐近稳定性.该方法扩展了以前的稳定性结果,保守性低,容易验证,同时也适用于其它类型的递归神经网络的稳定性分析.     

离散指数型BAM网络的记忆容量

基于Kosko的双向联想记忆模型BAM(bidirectional associative memory)原理,本文提出了一个离散指数型双向联想记忆模型.通过理论分析证实,该模型的记忆容量远远大于BAM的记忆容量.本文给出了指数型BAM记忆容量的下界.     

多重改进型指数双向联想记忆模型及其在多证据推理中的决策性能

提出了多证据推理中采用神经网络来模拟信念组合学习方法。网络由多个改进型指数双向联想记忆模型（IeBAM）构成,并且共享一个输出来同时进行多证据不确定性的管理。文中证明了多重IeBAM(Multi-IeBAM）的稳定性,讨论了在多条证据同时提交网络后的多数规则。理论和实验都证明了多数因子比Wang所提模型更紧凑、更严格,从而可保证在受一定程度的干扰下,专家们仍能做出正确决策。最后所给出的模拟例子的结果与直觉推理相吻合。     

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.     

具有内连接的指数多值双向联想记忆模型

C_CWang的多值指数双向联想记忆模型 (MVeBAM)是一种高存储容量的联想神经网络. 本文在MVe BAM的基础上通过引入自相关项 (或内连接 )提出了一个新的具有内连接的多值指数双向联想记忆模型, 推广了MVeBAM. 通过定义简单的能量函数证明了其在同、异步方式下的稳定性, 从而保证了所学模式对成为被推广的MVeBAM(EMVeBAM)的稳定点. 最后, 计算机模拟证实了EMVeBAM比MVeBAM具有更高的存储容量和更好的纠错性能.     

时滞标准神经网络模型及其应用

提出一种新的神经网络模型---时滞标准神经网络模型(DSNNM),它由线性动力学系统和有界静态时滞非线性算子连接而成.利用不同的Lyapunov泛函和S方法推导出DSNNM全局渐近稳定性和全局指数稳定性的充分条件,这些条件可表示为线性不等式(LMI)形式.大多数时滞(或非时滞)动态神经网络(DANN)稳定性分析或神经网络控制系统都可以转化为DSNNM,以便用统一的方法进行稳定性分析或镇定控制.从DSNNM应用于时滞联想记忆(BAM)神经网络的稳定性分析以及PH中和过程神经控制器的综合实例,可以看出,得到的稳定性判据扩展并改进了以往文献中的稳定性定理,而且可将稳定性分析推广到非线性控制系统的综合.     

采用分布式处理系统实现神经网络模型

本文着重研究了神经元网络模型中的BAM模型,并提出了用于直观地考察BAM状态的能量图.我们注意到BAM的性能是很弱的,所以又提出了改进模型r BAM,并证明了r BAM的性能比BAM优越。最后在单个PC和局网上实现了r BAM模型的软件模拟.     

An analysis of high-capacity discrete exponential BAM

An exponential bidirectional associative memory (eBAM) using an exponential encoding scheme is discussed. It has a higher capacity for pattern pair storage than conventional BAMs. A new energy function is defined. The associative memory takes advantage of the exponential nonlinearity in the evolution equations such that the signal-to-noise ratio (SNR) is significantly increased. The energy of the eBAM decreases as the recall process proceeds, ensuring the stability of the system. The increase of SNR consequently enhances the capacity of the BAM. The capacity of the exponential BAM is estimated.     

Performance analysis of the bidirectional associative memory and animproved model from the matched-filtering viewpoint

This paper discusses the bidirectional associative memory (BAM) model from the matched-filtering viewpoint and offers it a new interpretation. Our attention is focused on the problem of stability and attractivity of equilibrium states. Several sufficient and/or necessary conditions are presented. To improve the BAM performance, an exponential function is used to enhance the correlations between the binary vectors of the retrieval key and that of the stored pattern similar to the key. The modified model is shown to be asymptotically stable. Theoretical analysis and simulation results demonstrate that the modified model performs much better than the original BAM in terms of memory capacity and error correction capability.     

Data compression by the recursive algorithm of exponentialbidirectional associative memory

A novel data compression algorithm utilizing the histogram and the high-capacity exponential bidirectional associative memory (eBAM) is presented. Since eBAM has been proved to possess high capacity and fault tolerance, it is suitable to be utilized in the data compression using the table-lookup scheme. The histogram approach is employed to extract the feature vectors in the given data. The result of the simulation of the proposed algorithm turns out to be better than the traditional methods.     

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.     

多值指数式多向联想记忆模型

多向联想记忆MDAM(multidirectional associative memory)模型是Kosko双向联想记忆模型BAM(bidirectional associative memory)的一个直接推广,它可应用于数据融合及维数分裂,使模型能处理大维数输入问题.目前所提出的若干种多向模型均局限于二值输入/输出模式对,但如在图象处理等的实际应用中,所处理的模式均是多值的.本文的目的就是提出一个多值指数式多向联想记忆模型MVeMDAM(multivalued exponential multidi     

Global exponential stability for neutral-type BAM neural networks with time-varying delays

In this article, the global exponential stability of neutral-type bidirectional associative memory (BAM) neural networks with time-varying delays is analysed by utilizing the Lyapunov–Krasovskii functional and combining with the linear matrix inequality (LMI) approach. New sufficient conditions ensuring the global exponential stability of neutral-type BAM neural networks is obtained by using the powerful MATLAB LMI control toolbox. In addition, an example is provided to illustrate the applicability of the result.     

A deterministic capacity-finding method for multi-valuedexponential BAM

The multi-valued exponential bidirectional associative memory (MV-eBAM) has been proposed and proved to be asymptotically stable under certain constraints. Although multi-valued eBAM has been verified to possess high capacity by thorough simulations, the capacity is still unable to be solved analytically. In the paper, an algorithm is proposed to derive the capacity. Some important characteristics, including the absolute lower bound of the radix, and the approximate capacity are also discussed. The result shows that the multi-valued eBAM indeed possesses high capacity     

Global Exponential Stability of High-Order Bidirectional Associative Memory (BAM) Neural Networks with Proportional Delays

Neural Processing Letters - This paper considers the global exponential stability (GES) of high-order bidirectional associative memory (BAM) neural networks with proportional delays. Here,...