函数链式双向联想存储器

函数链式双向联想存储器陈松灿（南京航空航天大学计算机科学系南京２１００１６）ＢＩＤＩＲＥＣＴＩＯＮＡＬＡＳＳＯＣＩＡＴＩＶＥＭＥＭＯＲＹＷＩＴＨＦＵＮＣＴＩＯＮＡＬＬＩＮＫ￥ＣｈｅｎＳｏｎｇｃａｎ（ＤｅｐａｒｔｍｅｎｔｏｆＣｏｍｐｕｔｅｒＳｃｉｅｎｃ...     

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．     

在FoxBASE＋V2．10下实现热键输入

在ＦｏｘＢＡＳＥ＋Ｖ２．１０下实现热键输入太原重型机器厂阎亚林本文介绍了一种在对数据库进行汉字信息的录入和修改过程中采用的热键输入法。在实现方法上，主要是利用了命令ＯＮＫＥＹ＝（ｅｘｐＮ）［ＣＯＭＭＡＮＤ］，函数ＳＹＳ（１８）和热键数据库。命令ＯＮＫ...     

基于广义逆的指数型联想存储器模型

基于Ｋｏｈｏｎｅｎ的广义逆联想存储模型ＧＩＡＭ（ｇｅｎｅｒａｌｉｚｅｄｉｎｖｅｒｓｅａｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）和Ｍｕｒａｋａｍｉ的最小平方联想存储ＬＳＡＭ（ｌｅａｓｔｓｑｕａｒｅｓａｓｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）原理，本文提出了一个指数型联想存储器．该模型的存储性能经计算机模拟证实，远远优于ＧＩＡＭ和ＬＳＡＭ，通过适当地调节参数，几乎可达到完全的联想．对输入噪声方差，无需先验假设，同时还实现了一定程度的非线性映射特性.     

帝国时代Ⅱ密技

ＡｇｅｏｆＥｍｐｉｒｅｓ２在游戏进行中按下ＥＮ－ＴＥＲ键,然后在对话框中输入以下密码即可：ＲＯＣＫ　ＯＮ：得到１０００块石头ＬＵＭＢＥＲＪＡＣＫ：得到１０００块木头ＲＯＢＩＮＨＯＯＤ：得到１０００条金ＣＨＥＥＳＥＳＴＥＡＫＪＩＭＭＹ'Ｓ：得到１０００食物ＭＡＲＣＯ：显示地图ＰＯＬＯ：移动阴影ＡＥＧＩＳ：加快建筑ＮＡＴＵＲＡＬＷＯＮＤＥＲＳ：控制大自然ＨＥＳＩＧＮ：任务失败ＷＩＭＰＹＷＩＭＰＹ－ＷＩＭＰＹ：破坏自已ＩＬＯＶＥＴＨＥＭＯＮＫＥＹＩＩＥＡＤ：得到ＶＤＭＬＨＯＷＤＯＹＯＵＴＵＲＮＴＨＩＳ…     

容错型令牌总线网性能估计算法

容错型令牌总线网性能估计算法李忠勇，李人厚（西安交通大学自动化系西安７１００４９）ＡＰＥＲＦＯＲＭＡＮＣＥＥＶＡＬＵＡＴＩＯＮＡＬＧＯＲＩＴＨＭＯＦＦＡＵＬＴ－ＴＯＬＥＲＡＮＴＴＯＫＥＮＢＵＳＮＥＴＷＯＲＫ￥ＬｉＺｈｏｎｇｙｏｎｇａｎｄＬｉＲｅｎｈｏ...     

Navier－Stokes方程的非线性Galerkin有限元方法

Ｎａｖｉｅｒ－Ｓｔｏｋｅｓ方程的非线性Ｇａｌｅｒｋｉｎ有限元方法何银年，李开泰，向一敏（西安交通大学）ＮＯＮＬＩＮＥＡＲＧＡＬＥＲＫＩＮＦＩＮＩＴＥＥＬＥＭＥＮＴＭＥＴＨＯＤＯＦＮＡＶＩＥＲ－ＳＴＯＫＥＳＥＱＵＡＴＩＯＮＳ￥ＨｅＹｉｎ－ｎｉａｎ；Ｌｉ...     

一种并行计算K阶线性递归N方程组的新方法

一种并行计算Ｋ阶线性递归Ｎ方程组的新方法朱大铭，马绍汉，马军（山东大学计算机科学系、济南２５０１００）ＡＮＥＷＭＥＴＨＯＤＦＯＲＳＯＬＶＩＮＧＴＨＥＳＹＳＴＥＭＯＦＫ－ＴＨＯＲＤＥＲＬＩＮＥＡＲＲＥＣＵＲＲＥＮＣＥＥＱＵＡＴＩＯＮＳＩＮＰＡＲＡＬＬＥ...     

知识获取系统NDKAS的研究与应用

知识获取系统ＮＤＫＡＳ的研究与应用潘金贵，陈彬，陈晶，陈世福（南京大学计算机科学系南京２１０００８）ＴＨＥＲＥＳＥＡＲＣＨＡＮＤＡＰＰＬＩＣＡＴＩＯＮＯＮＴＨＥＫＮＯＷＬＥＤＧＥＡＣＱＵＩＳＩＴＩＯＮＳＹＳＴＥＭ－ＮＤＫＡＳ￥ＰａｎＪｉｎｇｕｉ；Ｃｈ...     

复平面上超越函数零点的数值计算

复平面上超越函数零点的数值计算龙云亮，文希理，谢处方（成都电子科技大学）ＡＮＩＭＰＬＥＭＥＮＴＡＴＩＯＮＯＦＡＲＯＯＴＦＩＮＤＩＮＧＡＬＧＯＲＩＴＨＭＦＯＲＴＲＡＮＳＣＥＮＤＥＮＴＡＬＦＵＮＣＴＩＯＮＳＩＮＡＣＯＭＰＬＥＸＰＬＡＮＥ￥ＬｏｎｇＹｕｎ－...     

Autonomous learning with complex dynamics

Traditionally, associative memory models are based on point attractor dynamics, where a memory state corresponds to a stationary point in state space. However, biological neural systems seem to display a rich and complex dynamics whose function is still largely unknown. We use a neural network model of the olfactory cortex to investigate the functional significance of such dynamics, in particular with regard to learning and associative memory. the model uses simple network units, corresponding to populations of neurons connected according to the structure of the olfactory cortex. All essential dynamical properties of this system are reproduced by the model, especially oscillations at two separate frequency bands and aperiodic behavior similar to chaos. By introducing neuromodulatory control of gain and connection weight strengths, the dynamics can change dramatically, in accordance with the effects of acetylcholine, a neuromodulator known to be involved in attention and learning in animals. With computer simulations we show that these effects can be used for improving associative memory performance by reducing recall time and increasing fidelity. the system is able to learn and recall continuously as the input changes, mimicking a real world situation of an artificial or biological system in a changing environment. © 1995 John Wiley & Sons, Inc.     

具有非线性映射能力的高性能联想记忆模型

通常的联想记忆模型的联想性能由于受到输入模式间交叉相关项的影响而有所下降，并且在输入与输出之间缺乏非线性映射能力。本文介绍一种高性能联想记忆模型，它将低维输入向量映射到一个高维的中间向量，从而提高了系统的联想能力，又使系统具有非线性映射能力，最后给出了几种推广。     

Some experiments on human memory and a new neural model

An associative memory with parallel architecture is presented. The neurons are modelled by perceptrons having only binary, rather than continuous valued input. To store m elements each having n features, m neurons each with n connections are needed. The n features are coded as an n-bit binary vector. The weights of the n connections that store the n features of an element has only two values -1 and 1 corresponding to the absence or presence of a feature. This makes the learning very simple and straightforward. For an input corrupted by binary noise, the associative memory indicates the element that is closest (in terms of Hamming distance) to the noisy input. In the case where the noisy input is equidistant from two or more stored vectors, the associative memory indicates two or more elements simultaneously. From some simple experiments performed on the human memory and also on the associative memory, it can be concluded that the associative memory presented in this paper is in some respect more akin to a human memory than a Hopfield model.     

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.     

Competition and cooperation in neuronal processing

A new type of model neuron is introduced as a building block of an associative memory. The neuron, which has a number of receptor zones, processes both the amplitude and the frequency of input signals, associating a small number of features encoded by those signals. Using this two-parameter input in our model compared to the one-dimensional inputs of conventional model neurons (e.g., the McCulloch Pitts neuron) offers an increased memory capacity. In our model, there is a competition among inputs in each zone with a subsequent cooperation of the winners to specify the output. The associative memory consists of a network of such neurons. A state-space model is used to define the neurodynamics. We explore properties of the neuron and the network and demonstrate its favorable capacity and recall capabilities. Finally, the network is used in an application designed to find trademarks that sound alike.     

混沌神经网络在分离叠加模式和多对多联想记忆中的应用

混沌是不含外加随机因素的完全确定性的系统表现出来的界于规则和随机之间的内秉随机行为。脑神经系统是由神经细胞组成的网络。类似于人脑思维的人工神经网络与冯·诺依曼计算机相比,在信息处理方面有很大的优越性。混沌和神经网络相互融合的研究是从90年代开始的,其主要的目标是通过分析大脑的混沌现象,建立含有混沌动力学的神经网络模型(即混沌神经网络模型),将混沌的遍历性、对初始值敏感等特点与神经网络的非线性、自适应、并行处理优势相结合,     

A Bidirectional Hetero-Associative Memory for True-Color Patterns

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.     

基于认知的联想记忆仿真研究

在对联想记忆神经网络的研究中,为提高现有联想记忆网络的存储能力以及相似模式和多值模式的联想成功率,提出了一种新的联想记忆网络。样本模式信息存储在动态权值矩阵中,网络根据不同的输入模式可自适应地调节当前权值矩阵。与传统联想网络相比,输入模式的信息不仅给出了联想记忆的初值,且在联想记忆过程中起到启发式搜索的作用,使网络的存储能力和联想成功率得到较好的改善。尤其可以有效地实现相似模式以及多值模式的联想记忆功能。仿真结果验证了方法的有效性。     

A New Associative Model with Dynamical Synapses

The brain is not a huge fixed neural network, but a dynamic, changing neural network that continuously adapts to meet the demands of communication and computational needs. In classical neural networks approaches, particularly associative memory models, synapses are only adjusted during the training phase. After this phase, synapses are no longer adjusted. In this paper we describe a new dynamical model where synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. We provide some propositions that guarantee perfect and robust recall of the fundamental set of associations. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model.     

Associative learning method in a hypercolumn model

We propose an associatively learnable hypercolumn model (AHCM). A hyper-column model is a self-organized, competitive, and hierarchical multilayer neural network. It is derived from the neocognitron by replacing each S cell and C cell with a two-layer hierarchical self-organizing map. The HCM can recognize images with variant object size, position, orientation and spatial resolution. However, feature maps may integrate some features extracted in the lower layer even if the features are extracted from input data which belong to different categories. The learning algorithm of the HCM causes this problem because it is an unsupervised learning used by a self-organizing map. An associative learning method is therefore introduced, which is derived from the HCM by appending associative signals and associative weights to traditional input data and connection weights, respectively. The AHCM was applied to hand-shape recognition. We found that the AHCM could generate an appropriate feature map and higher recognition accuracy compared with the HCM. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006