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。     

一种多晶硅高温压力传感器

制作压力传感器时，在二氧化硅层上淀积多晶硅膜，既可利用优良的机械特性，又可保证压敏电阻与衬底间具有良好的绝缘性，由此可大大提高器件的温度特性。介绍了一种多晶硅压力传感器的原理和设计。实验结果表明，这类传感器具有灵敏度好，精度高等特点，电路工作范围为０－２５０℃，且具有良好的温度稳定性。     

一种高分辨力柔性触觉传感器

本文描述了一种基于全内反射原理，用透明橡胶材料作为波导板的柔性触觉传感器。该传感器除具有一般刚性波导板触觉传感器的高分辨力等特点外，还具有柔性好，力灵敏阈值低，不怕碰撞等特点，该传感器特别适合装在智能机器人手爪上，为机器人系统高效率地获取物体形状，位置和姿态信息，实现智能抓握物体，操作物体等功能提供了有效的手段。     

机器人腕力传感器的动态负载效应

本文从时域和频域两方面研究机器人腕力传感器在加载情况下的动态响应，进行动态建模和频谱分析，探讨负载效应的影响。     

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。     

一个新的力觉临场感系统模型

本文提出了一个新的力觉临场感系统模型，讨论了它的二端口网络结构，推导了相应的网络特性参数，分析了参数的含仪和测量方法。此外，对理想系统的参数也进行了分析，本文的研究为临场感技术的发展提供了基础。     

冲击能传感器的研究与分析

本文分析并推导了应力波在弹性体中传播时,质点的运动速度、压力、位移及应力载波的聚流的形成与表达式.利用得到的公式描述了冲击能传感器的工作机理.给出了某风动工具的测试结果.     

微型真空力敏器件的研究

本文首次用电荷模拟法计算了真空微电子力敏传感器的场致发射阴极与阳极间的电场分布,同时计算了<100>晶向的方形硅薄膜受压形变与压力的关系,进而得出了这种传感器的电流-压力曲线.还做了水槽模拟电场分布的实验,理论与实验符合的很好.