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.     

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.     

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。     

通用绘图软件NuSlides中的面向对象的用户界面设计

本文描述在Ｕｎｉｘ环境下开发的通用绘图软件ＮｕＳｌｉｄｅ中的图形用户界面的设计思想和实际方法。其特点是基子面向对象的技术，提供多窗口、菜单驱动、选择面板、键盘输入等多种才法。该图形用户界面具有灵活性、可扩充性和易使用性。     

青霉素发酵过程的智能优化控制

本文在分析青霉素发酵补糖工艺的要求和存在的问题基础上,设计出智能控制算法,采用集散系统实现在线优化控制,经实际应用,效果明显。     

工业锅炉群集散控制系统

介绍了XMC-3000工业热水锅炉群集散控制系统的硬件组成及特点,描述了锅炉燃烧系统、定压系统的控制方案和系统供热调节模型,最后给出了系统的主要功能。     

用6805实现宽范围高精度频率测量

采用ＭＣ６８０５系列单片机并充分利用其定时器，选择测频法或测周法，可以实现定范围、高精度频率测量。此种方法只占用１个内部定时器，节省了硬件开销。     

一种基于阵列式触觉传感器的主动触觉搜索方法及仿真

本文在一种二指节手指模型的基础上，提出了一种新的主动触觉搜索策略和方法，综合利用阵列式触觉传感铭的输出图象来不断改变机械手的姿态，从而依次有序地搜索到三维物体的特征点，并据此生成物体的三维图形。此外还讨论了上述方法的计算机仿真实现。     

A STEP-BASED CAD/CAPP INTEGRATION

ＡＳＴＥＰ－ＢＡＳＥＤＣＡＤＩＣＡＰＰＩＮＴＥＧＲＡＴＩＯＮＺｈｅｎｇＬｉａｎｙｕ；ＤｕＰｅｉＡＳＴＥＰ－ＢＡＳＥＤＣＡＤＩＣＡＰＰＩＮＴＥＧＲＡＴＩＯＮ￥ＺｈｅｎｇＬｉａｎｙｕ；ＤｕＰｅｉＡｂｓｔｒａｃｔＴｈｅｉｎｆｏｒｍａｔｉｏｎｉｎｔｅｇｒａｔ...