时变容错域的感知联想记忆模型及其实现算法

提出并用软件实现了一种时变容错域的感知联想记忆模型．该模型有以下特点：（1）模拟了大脑联想记忆的容错域随时间变化的特点．模型可以根据记忆样本的重要性，为记忆样本在不同时间设计适当的容错域;（2）实现了”维到m维空间的无穷值模式非线性联想，而且该模型的样本容错域充满整个实空间R^n，模型没有伪吸引子；（3）联想记忆速度快．     

联想记忆神经网络的一个有效学习算法

提出一种新的联想记忆网络模型的有效学习算法,它具有下述特点:(1)可以全部存 储任意给定的训练模式集,即对于训练模式的数目和它们之间相关性的强弱没有限制;(2)最 小的训练模型吸引域达到最大;(3)在(2)的基础上,每个训练模式具有尽可能大的吸引域; (4)联想记忆神经网络是全局稳定的.大量的计算机仿真实验结果充分说明所提出的学习算 法比已有算法具有更强的存储能力和联想容错能力.     

基于模糊神经网络结构的多值联想记忆

文章基于模糊神经网络结构,即通过模糊化,推理,去模糊三个过程,把Kosko提出的模糊联想记忆(FAM)网络模型应用到容错性需要较强的多值联想记忆中,解决了这种网络模型不能对随机噪声模式正确联想的问题,新的网络模型设计简单,大量实验表明文中的联想记忆网络大大提高了FAM网络的容错性能。     

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。     

复形态双向联想记忆网络及其性能分析

基于复数环(C,∨,∧,+)提出了复形态双向联想记忆网络模型,给出了神经网络实现双向完全联想记忆的条件,并分析了自联想的存储能力、稳定性和收敛性及神经网络的抗噪声能力.当复数虚部取零时即为实数,实数域形态双向联想记忆网络就是复数域形态双向联想记忆网络的特例.最后通过仿真实验验证复形态双向联想记忆网络的有效性.     

基于人工免疫网络的联想记忆器

本文提出一种利用人工免疫网络模型设计联想记忆器的方法，着重指出通过定义适当的补充机制形式，人工免疫系统如何应用于解决联想记忆问题，仿真结果与其他熟知的模型所得结果进行了比较。     

一种基于稀疏分布记忆模型的汉字联想记忆方法

本文描述了Ｋａｎｅｒｖａ的稀疏分布记忆模型，指出了它在用于汉字联想时的问题，同时提出了改进的模型，试验表明，这种改进模型使记忆容量和容错能力大大提高。     

含时滞记忆的非线性时滞系统满意容错控制

针对一类基于T-S模糊模型描述的非线性时滞系统,研究在一般执行器故障模式下的含时滞记忆的鲁棒H∞容错控制器设计问题.针对任意连续型执行器故障模式,采用并行分布式补偿原理设计含记忆型状态反馈控制器,给出非线性时滞系统在执行器发生故障情况下的鲁棒镇定准则.然后给出H∞性能指标约束下的满意容错控制器的设计方法和设计步骤.提出的含时滞记忆的状态反馈控制方法可以确保当执行器发生故障时,闭环系统不仅具有渐近稳定性,而且有一定的抗扰动性能,状态反馈控制器设计的保守性较不含时滞记忆控制器设计方法大大降低.仿真实例验证了鲁棒容错控制策略的有效性.     

一种新的双向联想记忆神经网络学习算法

文章提出了一种基于遗传算法的按`位'加权双向联想记忆神经网络(BAM)的学习算法.根据判定BAM网络稳定模式和容错能力的充分条件,推出了求取按位加权BAM加权系数的优化目标函数,之后作者给出了求解此目标函数的遗传算法.二值图象模式存储、联想记忆的计算机实验结果表明,文中所提出的方法能有效地提高网络的存储能力和容错能力.     

内连式复值双向联想记忆模型及性能分析

Lee的复域多值双向联想记忆模型(complex domain bidirectional associative memory,简称CDBAM)不仅将Kosko的实域BAM(bidirectional associative memory)推广至复域,而且推广至多值情形,以利于多值模式(如灰级图像等)间的联想.在此基础上,提出了一个新的推广模型:复域内连式多值双向联想记忆模型(intraconnected CDBAM,简称ICDBAM),通过定义的能量函数证明了它在同步与异步更新方式下的稳定性,从而保证所有训练样本对成为其稳定点,克服了CDBAM所存在的补码问题.计算机模拟证明了该模型比CDBAM具有更高的存储容量和更好的纠错性能.     

Symbolic synthesis of masking fault-tolerant distributed programs

We focus on automated addition of masking fault-tolerance to existing fault-intolerant distributed programs. Intuitively, a program is masking fault-tolerant, if it satisfies its safety and liveness specifications in the absence and presence of faults. Masking fault-tolerance is highly desirable in distributed programs, as the structure of such programs are fairly complex and they are often subject to various types of faults. However, the problem of synthesizing masking fault-tolerant distributed programs from their fault-intolerant version is NP-complete in the size of the program’s state space, setting the practicality of the synthesis problem in doubt. In this paper, we show that in spite of the high worst-case complexity, synthesizing moderate-sized masking distributed programs is feasible in practice. In particular, we present and implement a BDD-based synthesis heuristic for adding masking fault-tolerance to existing fault-intolerant distributed programs automatically. Our experiments validate the efficiency and effectiveness of our algorithm in the sense that synthesis is possible in reasonable amount of time and memory. We also identify several bottlenecks in synthesis of distributed programs depending upon the structure of the program at hand. We conclude that unlike verification, in program synthesis, the most challenging barrier is not the state explosion problem by itself, but the time complexity of the decision procedures.     

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.     

Behavioural study of median associative memory under true-colour image patterns

Median associative memories (MED-AMs) are a special type of associative memory that substitutes the maximum and minimum operators of a morphological associative memory with the median operator. This associative model has been applied to restore grey scale images and provided a better performance than morphological associative memories when the patterns are altered with mixed noise. Despite their power, MED-AMs have not been adopted in problems related with true-colour patterns. In this paper, we describe how MED-AMs can be applied to problems involving true-colour patterns. Furthermore, a complete study of the behaviour of this associative model in the restoration of true-colour images is performed using a benchmark of 16,000 images altered by different noise types.     

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.     

Evolutionary Model of Optimization of Modular Associative Memory for Dataflow Machines Based on Genetic Algorithm

An evolutionary model of modular associative memory for machines with dataflow architecture is suggested. A problem of determination of optimal allocation of a dataflow in a computational system with modular associative memory is formulated. The model suggested is based on graph representation of the dataflow. The allocation of the dataflow among modules is realized by means of a hash function. A method for searching for optimal hashing with the use of a genetic algorithm is suggested. The convergence of the genetic algorithm is studied. Estimates of optimal allocation among modules of associative memory for various computational problems are obtained.     

训练模式摄动对模糊形态学联想记忆网络的影响

众多学者研究的两类形态学联想记忆网络的存储能力、抗腐蚀/膨胀噪声的能力等性质几乎都相同。但是文中研究发现两类网络对训练模式摄动的鲁棒性差异很大。一类对训练模式摄动拥有好的鲁棒性,而另一类则较差。该研究结论能为形态学联想记忆网络的学习算法选择和训练模式采集设备的精度要求提供指导,对前期训练模式的获取过程提供警示。     

An RCE-based Associative Memory with Application to Human Face Recognition

Many models of neural network-based associative memory have been proposed and studied. However, most of these models do not have a rejection mechanism and hence are not practical for many real-world associative memory problems. For example, in human face recognition, we are given a database of face images and the identity of each image. Given an input image, the task is to associate when appropriate the image with the corresponding name of the person in the database. However, the input image may be that of a stranger. In this case, the system should reject the input. In this paper, we propose a practical associative memory model that has a rejection mechanism. The structure of the model is based on the restricted Coulomb energy (RCE) network. The capacity of the proposed memory is desibed by two measures: the ability of the system to correctly identify known individuals, and the ability of the system to reject individuals who are not in the database. Experimental results are given which show how the performance of the system varies as the size of the database increases up to 1000 individuals.     

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     

约束最小平方联想存储器及性能比较

鉴于Ｋｏｈｏｎｅｎ的最佳联想存储器对带噪输入会产生难以接受的联想误差，文中试图通过在Ｋｏｈｏｎｅｎ模型中引入对连接权阵的某种约束并进而优化，使修改后的Ｋｏｈｏｎｅｎ模型（ＣＬＳＡＭ）对带噪输入具有最小误差的联想．借助奇异值分解（ＳＶＤ）理论的分析和计算机模拟证实了ＣＬＳＡＭ的性能优越性．