A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

光纤通信在市内接入网的应用

先说明市内接入网的单位用户和住家用户两大类使用通信和电视、交互型和分配型业务的要求不相同，相应地对光纤传输的应用须作不同的考虑，文中认为；光纤从市内交换局直连办公大楼是合乎现实需要的，而对于居民住家，宜选用光纤连至路边或光纤与同轴结合和适当利用无源光网络等方案，俟将来时机成熟才实行光纤连至每一家，文末有概括总结，说明接入网不同于长途网，接入网的光纤系统可以使用常规单模光纤，工作于波长1．3μm，而光放大器和波分多路系统不是普遍需要，但光电子集成在光端机中是必要的。     

广东省智能网工程建设

本文介绍了广东省智能网的组网方案及所能提供的业务,并论述了智能网工程建设,安装维护以及与广东省现有移动通信网联网中存在的一些问题。     

提高前馈神经网络推广能力的若干实际方法

提高前馈神经网络的推广能力是深受关注的问题，本文根据我们最近提出了一个网络有效推广的准则，从提高网络特征提取能力，分类能力和修改神经元激活函数等几方面给出了若干实际方案，我们在任意手写数字识别问题上的实验结果证实了这些方法的有效性。     

高氧化态（Ⅱ─Ⅳ）三核金属簇合物构型的人工神经网络判别方法

本文运用一典型的人工神经网络模型─“反向传播”模型，对高氧化态（Ⅱ─Ⅳ）三核金属簇合物的构型分布进行了分析，得到了较好的分类、预报结果为化合物结构分析提供了新的工具。     

多Transputer系统上的并行消隐算法及其图形显示

本文讨论了三维物体隐面消除的并行处理问题。给出了一类ＭＩＭＤ并行深度缓冲器算法，并在多Ｔｒａｎｓｐｕｔｅｒ系统上实现。文中还对这些算法的效率进行了比较。     

Windows系统中两种动态交换数据的方法

本文介绍了Ｗｉｎｄｏｗｓ环境下基于消息的ＤＤＥ和基于动态链接库的ＤＤＥＭＬ的区别，并详细叙述了如何使用ＤＤＥＭＬ编制Ｗｉｎｄｏｗｓ应用程序。     

实用神经网络的生物模拟器 待续

本文介绍一种能模拟单个神经元、触突以及系统的模拟器Ｓｉｍｕｌａｔｏｒ。用它模拟在生物界所熟知的一种无脊椎软体类似海参的食肉类动作Ｐｌｅｕｒｏ－ｂｒａｎｃｈｅａ。它的各种形为如吞食食物，遇敌退缩等用Ｆｏｒｔｒａｎ７７版本编程并在Ｓｕｎ３／５０工作站上进行模拟获得了满意结果。