Complex-valued multistate neural associative memory

A model of a multivalued associative memory is presented. This memory has the form of a fully connected attractor neural network composed of multistate complex-valued neurons. Such a network is able to perform the task of storing and recalling gray-scale images. It is also shown that the complex-valued fully connected neural network may be considered as a generalization of a Hopfield network containing real-valued neurons. A computational energy function is introduced and evaluated in order to prove network stability for asynchronous dynamics. Storage capacity as related to the number of accessible neuron states is also estimated.     

Characteristics of the Hopfield associative memory utilizingisomorphism relations

Isomorphism relations are utilized to analyze the Hopfield associative memory. When the number of fundamental memories m=/<3, it is proved that two Hopfield associative memories are isomorphic if they have the same mutual distances between the fundamental memories. The number of stable states and the synchronous convergence time of a Hopfield associative memory are shown to be less than or equal to 2 to the power 2(m-1) and 4 to the power 2(m-1), respectively, where m>/=1.     

A new neural associative memory model

We present a new associative memory model based on the Hamming memory, but where the winner-take-all network part is replaced by a layer of nodes with somewhat complex node functions. This new memory can produce output vectors with individual “don't know” bits. the simulations demonstrate that this memory model works appropriately. © 1992 John Wiley & Sons, Inc.     

A Boolean Hebb rule for binary associative memory design

A binary associative memory design procedure that gives a Hopfield network with a symmetric binary weight matrix is introduced in this paper. The proposed method is based on introducing the memory vectors as maximal independent sets to an undirected graph, which is constructed by Boolean operations analogous to the conventional Hebb rule. The parameters of the resulting network is then determined via the adjacency matrix of this graph in order to rind a maximal independent set whose characteristic vector is close to the given distorted vector. We show that the method provides attractiveness for each memory vector and avoids spurious memories whenever the set of given memory vectors satisfy certain compatibility conditions, which implicitly imply sparsity. The applicability of the design method is finally investigated by a quantitative analysis of the compatibility conditions.     

CLAM: A new model of associative memory

We present a new associative memory model that stores arbitrary bipolar patterns without the problems we can find in other models like BAM or LAM. After identifying those problems we show the new memory topology and we explain its learning and recall stages. Mathematical demonstrations are provided to prove that the new memory model guarantees the perfect retrieval of every stored pattern and also to prove that whatever the input of the memory is, it operates as a nearest neighbor classifier. ©2000 John Wiley & Sons, Inc.     

An energy function-based design method for discrete hopfield associative memory with attractive fixed points

An energy function-based autoassociative memory design method to store a given set of unipolar binary memory vectors as attractive fixed points of an asynchronous discrete Hopfield network (DHN) is presented. The discrete quadratic energy function whose local minima correspond to the attractive fixed points of the network is constructed via solving a system of linear inequalities derived from the strict local minimality conditions. The weights and the thresholds are then calculated using this energy function. If the inequality system is infeasible, we conclude that no such asynchronous DHN exists, and extend the method to design a discrete piecewise quadratic energy function, which can be minimized by a generalized version of the conventional DHN, also proposed herein. In spite of its computational complexity, computer simulations indicate that the original method performs better than the conventional design methods in the sense that the memory can store, and provide the attractiveness for almost all memory sets whose cardinality is less than or equal to the dimension of its elements. The overall method, together with its extension, guarantees the storage of an arbitrary collection of memory vectors, which are mutually at least two Hamming distances away from each other, in the resulting network.     

自适应联想记忆细胞神经网络的优化设计

针对训练自适应联想记忆细胞神经网络(AM-CNN)过程收敛慢,设计出的网络抗噪性能不高的特点,通过融合蚁群优化算法和粒子群算法的思想,提出以目标网络对噪声模式的输出误差为目标函数,在目标函数的一个阈值分成的两个区间内,分别采取局部搜索和全局搜索策略,训练出AM-CNN的克隆模板的设计方法。数字模拟表明,与以往的设计方法相比,该算法能在细胞神经网络4~6次的迭代过程中稳定输出期望模式,收敛速度更快,设计出的AM-CNN性能比较稳定,并对噪声鲁棒,对高斯噪声N(0,0.8)准确率达到90%左右。     

CMOS current-mode neural associative memory design with on-chiplearning

Based on the Grossberg mathematical model called the outstar, a modular neural net with on-chip learning and memory is designed and analyzed. The outstar is the minimal anatomy that can interpret the classical conditioning or associative memory. It can also be served as a general-purpose pattern learning device. To realize the outstar, CMOS (complimentary metal-oxide semiconductor) current-mode analog dividers are developed to implement the special memory called the ratio-type memory. Furthermore, a CMOS current-mode analog multiplier is used to implement the correlation. The implemented CMOS outstar can on-chip store the relative ratio values of the trained weights for a long time. It can also be modularized to construct general neural nets. HSPICE (a circuit simulator of Meta Software, Inc.) simulation results of the CMOS outstar circuits as associative memory and pattern learner have successfully verified their functions. The measured results of the fabricated CMOS outstar circuits have also successfully confirmed the ratio memory and on-chip learning capability of the circuits. Furthermore, it has been shown that the storage time of the ratio memory can be as long as five minutes without refreshment. Also the outstar can enhance the contrast of the stored pattern within a long period. This makes the outstar circuits quite feasible in many applications.     

A feedforward bidirectional associative memory

In contrast to conventional feedback bidirectional associative memory (BAM) network models, a feedforward BAM network is developed based on a one-shot design algorithm of O(p(2)(n+m)) computational complexity, where p is the number of prototype pairs and n, m are the dimensions of the input/output bipolar vectors. The feedforward BAM is an n-p-m three-layer network of McCulloch-Pitts neurons with storage capacity 2(min{m,n}) and guaranteed perfect bidirectional recall. The overall network design procedure is fully scalable in the sense that any number p=/<2(min{m,n}) of bidirectional associations can be implemented. The prototype patterns may be arbitrarily correlated. With respect to inference performance, it is shown that the Hamming attractive radius of each prototype reaches the maximum possible value. Simulation studies and comparisons illustrate and support these theoretical developments.     

SoC嵌入式存储器内建自修复方法

嵌入式存储器的内建自测试及修复是提高SoC芯片成品率的有效办法。详细描述了存储器良率的评估方法,提出了一种基于Mentor公司Tessent工具的存储器修复结构。该结构采用了冗余修复及电可编程熔丝eFuse硬修复的方法,具有很好的通用性及可行性,已多次应用在实际项目中。     

Encoding method for bidirectional associative memory usingprojection on convex sets

The traditional encoding method of bidirectional associative memory (BAM) suggested by Kosko (1988) is based on the correlation method with which the capacity is very small. The enhanced Householder encoding algorithm (EHCA) presented here is developed on the basis of the Householder encoding algorithm (HCA) and projection on convex sets (POCS). The capacity of BAM with HCA tends to the dimension of the pattern pairs. Unfortunately, in BAM with HCA there are two different interconnection matrices and hence BAM with HCA may not converge when the initial stimulus is not one of the library patterns. In EHCA the two matrices found by HCA are reduced into one matrix by POCS. Hence, the convergent property of BAM can be maintained. Simulation results show that the capacity of BAM with EHCA is greatly improved.     

神经网络关联存储器的设计及应用

神经网络的存储能力一直是一个重大的缺陷,其存储主要体现在权重系数上,因此参数量一多,训练起来就十分困难。给神经网络设计一个外部关联存储器,能有效对神经网络的输入进行关联查询,并将查询的结果作为辅助输入传入到神经网络中去。此外,设计了自然语言语句的向量嵌入模型,并将模型和关联存储器集合起来形成一个自动关联语句语义向量的关联存储系统,其性能指标达到了设计要求。     

A new complex-valued formulation and eigenvalue analysis of the Helmholtz equation by boundary element method

By considering the close relationship between the multiple reciprocity boundary element formulation and that of the fundamental solution of the Helmholtz differential operator, we present a new complex-valued integral equation formulation for the eigenvalue analysis of the scalar-valued Helmholtz equation. Eigenvalues are determined as local minima of the determinant of the coefficient matrix of the discretized equation iteratively by the Newton scheme. The necessary recurrence formula is derived and computed with high efficiency, due to polynomial representation of the matrix components. Some example computations demonstrate the utility of the proposed formulation and eigenvalue determination scheme, and construction of adaptive boundary elements for the eigenvalue determination is attempted.     

A modified Hopfield auto-associative memory with improved capacity

This paper describes a new procedure to implement a recurrent neural network (RNN), based on a new approach to the well-known Hopfield autoassociative memory. In our approach a RNN is seen as a complete graph G and the learning mechanism is also based on Hebb's law, but with a very significant difference: the weights, which control the dynamics of the net, are obtained by coloring the graph G. Once the training is complete, the synaptic matrix of the net will be the weight matrix of the graph. Any one of these matrices will fulfil some spatial properties, for this reason they will be referred to as tetrahedral matrices. The geometrical properties of these tetrahedral matrices may be used for classifying the n-dimensional state-vector space in n classes. In the recall stage, a parameter vector is introduced, which is related with the capacity of the network. It may be shown that the bigger the value of the ith component of the parameter vector is, the lower the capacity of the [i] class of the state-vector space becomes. Once the capacity has been controlled, a new set of parameters that uses the statistical deviation of the prototypes to compare them with those that appear as fixed points is introduced, eliminating thus a great number of parasitic fixed points.     

利用离散Hopfield网络的码本设计方法

论文提出了一种利用Hopfield网络的码本设计方法,分析了LBG算法和离散Hopfield网络的特点,针对该特点构造聚类表格,并按离散Hopfield神经网络串行方式运行,从而得到最终码字集。通过实验表明,在码本大小相同的情况下,峰值信噪比提高了2.742～3.825 dB,生成的码本质量较传统的LBG算法更加有效。     

A new method for the design of energy transfer filters

This paper is concerned with the development of a new method for the design of energy transfer filters (ETFs). ETFs are a new class of non-linear filters recently proposed by the authors, which employ non-linear effects to transfer signal energy from one frequency band to a different frequency location. The new method uses the powerful orthogonal least squares (OLS) algorithm to solve the least squares problem associated with the design and compared with previous methods achieves much better filtering performance.     

一种新型的滑模变结构增益调度控制器设计方法

研究影响一般滑动模态变结构控制性能的因素,并给出了根据切换函数选择滑动模态系数、边界层厚度以及控制器增益系数的一般要求．针对一类含参数不确定性的非线性系统,采用新型增益调度变结构控制策略进行控制,以切换函数作为调度变量对滑动模态系数、边界层厚度以及控制器增益系数进行调度,以提高滑动模态变结构控制系统的控制性能,抑制颤振,降低控制能耗．仿真算例验证了所提出控制策略的有效性．     

参数化模糊联想记忆网络的学习算法

基于Dubois提出的带参数ξ的t-模T_ξ,提出了一种参数化的广义模糊联想记忆网络Max-T_ξ FAM。由于T_ξ中参数ξ的作用,在应用中Max-T_ξ FAM有更强的可调性和灵活性。接着利用T_ξ的伴随蕴涵算子,提出了Max-T_ξ FAM的一种有效学习算法。从理论上严格证明了,只要Max-T_ξ FAM能完整可靠地存储所给的多个模式对,则所提出的学习算法一定能找到使得网络能完整可靠存储这些模式对的所有连接权矩阵的最大者。最后,用实验说明了所提出的学习算法的有效性。     

On multiple training for bidirectional associative memory

The minimal number of times for using a pair for training to guarantee recall of that pair among a set of training pairs is derived for a bidirectional associative memory.     

Two coding strategies for bidirectional associative memory

Enhancements of the encoding strategy of a discrete bidirectional associative memory (BAM) reported by B. Kosko (1987) are presented. There are two major concepts in this work: multiple training, which can be guaranteed to achieve recall of a single trained pair under suitable initial conditions of data, and dummy augmentation, which can be guaranteed to achieve recall of all trained pairs if attaching dummy data to the training pairs is allowable. In representative computer simulations, multiple training has been shown to lead to an improvement over the original Kosko strategy for recall of multiple pairs as well. A sufficient condition for a correlation matrix to make the energies of the training pairs be local minima is discussed. The use of multiple training and dummy augmentation concepts are illustrated, and theorems underlying the results are presented.