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。     

混沌神经网络及其在联想记忆中的应用

提出了一种新型的混沌神经网络模型及其相应的学习算法。该学习算法利用了输入模式各元素之间的关联性信息。此文的神经网络可以用于多值模式的联想记忆,与现有的混沌神经网络相比,具有更好的性能。     

Artificial Neural Networks Manipulation Server: Research on the Integration of Databases and Artificial Neural Networks

This paper proposes a new whole and distributed integration approach between Artificial Neural Networks (ANNs) and Databases (DBs) taking into account the different stages of the former’s lifecycle (training, test and running). The integration architecture which has been developed consists of an ANN Manipulation Server (AMS) based on a client-server approach, which improves the ANNs’ manipulation and experimentation capabilities considerably, and also those of their training and test sets, together with their modular reuse among possibly remote applications. Moreover, the chances of integrating ANNs and DBs are analysed, proposing a new level of integration which improves the integration features considerably. This level has not been contemplated yet at full reach in any of the commercial or experimental tools analysed up to the present date. Finally, the application of the integration architecture which has been developed to the specific domain of Environmental Impact Assessments (EIAs) is studied. Thus, the versatility and efficacy of that architecture for developing ANNs is tested. The enormous complexity of the functioning of the patterns which rule the environment’s behaviour, and the great number of variables involved, make it the ideal domain for experimenting on the application of ANNs together with DBs.     

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.     

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。     

Case-Based Reasoning System and Artificial Neural Networks: A Review

In this survey paper, the-state-of-art of the connectionist model (i.e. Artificial Neural Network (ANN)) based methodology for a Case-Based Reasoning (CBR) system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. Several ANN models proposed for constructing a CBR system and for solving some special issues involved in a CBR process are described. The main characteristics of each model are analysed, and the advantages and limitations of different models are compared. Also, future research directions are outlined.     

基于暂态混沌神经网络的组播路由算法

讨论了高速包交换计算机网络中具有端到端时延的组播路由问题。首先给出了这类问题的网络模型及其数学描述，然后提出了基于暂态混沌神经网络的组播路由算法。实验结果表明，该算法能够快速有效地实现组播路由优化，并且计算性能及解的质量优于基于Hopfield神经网络的路由算法。     

一种新的基于混沌神经网络的组播路由算法

探讨了在高速包交换计算机网络中,具有端到端时延及时延抖动限制的组播路由问题,提出了基于混沌神经网络的组播路由优化算法。所提出的方法具有许多优良特性,即暂态混沌特性和平稳收敛特性,能有效地避免传统Hopfield神经网络极易陷入局部极值的缺陷。它通过短暂的倒分叉过程,能很快进入稳定收敛状态。通过计算机仿真,和其它的一些方法进行了对比,结果表明：该算法能根据组播应用对时延和时延抖动的要求,快速、有效地构造最优组播树,具有较强的实时性。     

基于混沌神经网络的最短路径路由算法

飞速发展的计算机网络对路由算法的反应速度提出了更高的要求．神经网络作为一种新的组合优化计算工具。在网络路由方面的应用得到较大关注．与传统的采用串行执行方式的算法相比，神经网络路由算法以其固有的并行执行方式，以及潜在的硬件实施能力，将成为这一领域的有力竞争者．由此提出了一种基于混沌神经网络的最短路径路由算法．仿真结果表明，该算法能有效克服Hopfield神经网络易陷入局部最优解的缺点，并且在收敛速度方面有了很大改进．     

人工神经网络的符号解释

提出了人工神经网络符号解释的基本过程，并详细阐述了该过程中的三个重要步骤：网络的构建、规则的提取以及规则的评估。     

混沌神经网络模型及其应用研究综述

回顾了近年来混沌神经网络模型及其应用的研究进展．首先依据混沌产生的机理,将现有的多种类型混沌神经网络模型归结为4类典型的网络模型,并结合各种网络模型的数学描述来分析各自的机理和特性;然后从复杂问题优化、联想记忆和图像处理、网络与通信、模式识别、电力系统负荷建模和预测5个方面,介绍了混沌神经网络的应用现状;最后评述了混沌神经网络今后的研究方向和研究内容．     

一种基于群体智能的人工神经网络学习算法

将群体智能优化理论引入一种前馈式人工神经网络——径向基函数（RBF）神经网络的学习训练过程,提出了基于智能微粒群算法的RBF神经网络学习算法,并与传统RBF神经网络学习算法进行了比较,实验结果证明了该方法的有效性。     

Modeling Axonal Plasticity in Artificial Neural Networks

Neural Processing Letters - Axonal growth and pruning are the brain’s primary method of controlling the structured sparsity of its neural circuits. Without long-distance axon branches...     

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.     

混沌神经网络应用于ATM路由算法的研究

ATM网络中基于给定的VP拓扑结构,提出一种暂态混沌神经网络模型的VC路由算法,通过构造能量函数达到网络资源的有效利用以及路由请求的有效性.仿真表明该算法能根据用户提出的VCC请求,实时、有效地实现VC路由选择和利用网络资源,达到一种路由的全局性能优化.     

混沌神经网络应用于ATM路由算法的研究

ATM网络中基于给定的VP拓扑结构,提出一种暂态混沌神经网络模型的VC路由算法,通过构造能量函数达到网络资源的有效利用以及路由请求的有效性。仿真表明该算法能根据用户提出的VCC请求,实时、有效地实现VC路由选择和利用网络资源,达到一种路由的全局性能优化。     

Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks

Pattern Analysis and Applications -     

Analysis of Fault Tolerance in Artificial Neural Networks

Wide attention was recently given to the problem of fault-tolerance in neural networks; while most authors dealt with aspects related to specific VLSI implementations, attention was also given to the intrinsic capacity of survival to faults characterizing the neural modes. The present paper tackles this second theme, considering in particular multilayered feed forward nets. One of the main goals is to identify the real influence of faults on the neural computation in order to show that neural paradigms cannot be considered intrinsically fault tolerant (i.e., able to survive to faults, even several of the most common and simple ones). A high abstraction level (corresponding to the neural graphs) is taken as the basis of the study and a corresponding error model is introduced. The effects of such errors induced by faults are analytically derived to verify the probability of intrinsic masking in the final neural outputs. Then, conditions allowing for complete compensation of the errors induced by faults through weight adjustment are evaluated to test the masking abilities of the network. The designer of a neural architecture should perform such a mathematical analysis to check the actual fault-tolerance features of his or her system. Unfortunately, this involves a very high computational overhead. As a cost-effective alternative for the designer, the use of a behavioral simulation is proposed for a quantitative evaluation of the error effect on the neural computation. Repeated learning (i.e., a new application of the learning procedure on the faulty network) is then experimented to induce error masking. Experimental results prove that even single errors affect the computation in a relevant way and that weight redistribution is not able to induce complete masking after a fault occurred, i.e., the network cannot be considered per se intrinsically fault tolerant and it is not possible to rely on learning only in order to achieve complete masking abilities. Mapping criteria of physical faults onto the abstract errors are finally examined to show the usability of the proposed analysis in evaluating the actual robustness of a neural networks' implementation and in identifying the critical areas where architectural redundancy should be introduced to achieve fault tolerance.     

一类时滞混沌忆阻器神经网络的延迟反同步控制

研究了一类时滞混沌忆阻器神经网络的延迟反同步控制问题．通过构造李亚普诺夫函数及采用微分包含理论和Halanay不等式的研究方法,设计了一个线性反馈控制器,并恰当选择控制器增益实现了一类混沌忆阻器神经网络驱动系统与响应系统之间的延迟反同步,所设计的控制器简单并易于实现．最后,仿真例子验证了所设计的控制器的有效性．