Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

美籍华裔专家讲通信工程技术新动态

本文扼要介绍１９９５年北京中美工程技术研讨会通信组五位美籍华裔专家关于通信技术新动态的专题报告。一是宽带综合业务数字网的同步数字系列（ＳＤＨ）和异步转移模式（ＡＴＭ）；二是光纤通信系统的光放大器（ＯＦＡ）、波分多路（ＷＤＭ）、光电子集成（ＯＥＩＣ）、色散补偿和新型光纤；三是蜂窝网车载移动通信和全国性个人通信业务（ＰＣＳ）、时分多址（ＴＤＭＡ）和码分多址（ＣＤＭＡ），四是智能网（ＩＮ）体系结构、概念模型和业务管理系统（ＳＭＳ）；五是数字传输网的恢复（ＦＡＳＴＡＲ）、维护（ＴＭＰ）和供应（ＴＳＮＯＷ）三种管理平台，及综合网络管理系统（ＩＮＭＳ）。     

SMS——智能网的业务管理系统

本文叙述了智能网的业务管理系统所实现的３种功能：业务管理功能，网络管理功能和业务管理访问功能，还介绍了业务管理和网络管理之间的关系，ＳＭＳ的集中式和分布式两种体系结构及业务管理功能的设计方法和进一步研究方向。     

离散Hopfield神经网络和联想记忆的开关电流技术实现

研究了离散Ｈｏｐｆｉｅｌｄ神经网络（ＤＨＮＮ）和联想记忆神经网络的开关电流技术实现，利用多权输入跨导，开关电流延迟器（ＳＩＤ）和可编程电流比较器（ＰＣＣ）实现了离散Ｈｏｐｉｅｌｄ神经网络，并提出了利用离散Ｈｏｐｆｉｅｌｄ神经网络实现自联想记忆时相应的开关电流电路，所提出了开关电流神经网络适宜于超大规模集成，能在低电压（如３．３Ｖ）下工作。     

微机字处理网络系统智能接口的设计

本文介绍了微机字处理系统网络联机的智能接口设计方案，采用比较简单而独特的手法，解决了通信控制和资源共享问题．     

Using neural networks to learn hand-eye co-ordination

The work presented in this paper shows how the association of proprioceptive and exteroceptive stimuli can enable a Kohonen neural network, controlling a robot arm, to learn hand-eye co-ordination so that the arm can reach for and track a visually presented target. The approach presented in this work assumes no a priorimodel of arm kinematics or of the imaging characteristics of the cameras. No explicit representation, such as homogeneous transformations, is used for the specification of robot pose, and camera calibration and triangulation are done implicitly as the system adapts and learns its hand-eye co-ordination by experience. This research is validated on physical devices and not by simulation.     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

基于网络环境的“任务驱动法”在Photoshop教学中的探索与实践

本文主要探讨在网络环境下,采用任务驱动教学法如何对艺术类学生进行Photoshop平面设计课程教学,从而满足在有限的课时中把计算机基础教育与专业教育结合,面向应用,提高学生的学习兴趣,培养学生的全面素质这一教学目标。     

Solution of temperature fields in hydrodynamics bearings by the numerical network method

Solution to the 2-D steady-state nonlinear heat conduction equation, involving cylindrical coordinates, applied to a plain bearing by a simple and versatile numerical technique based on network method is presented in this work. These advantages of the technique are necessary for the status evaluation of industrial machines during operation because time and computing resources are limited. The pressure field in the fluid is previously solved from the Reynolds equation by finite element method. The main difficulty is associated with the exponential dependency of the viscosity on temperature. The proposed model is very efficient and requires negligible computing times. Solutions are very close to the experimental and numerical results of other researchers.     

基于遗传神经网络的电站锅炉飞灰含碳量预测

针对锅炉飞灰含碳置影响因素复杂．实时测量难度大．精度低，提出了一种基于遗传神经网络的预测方法，并用于某电厂，结果表明，该方法是有效的。