Spin probe study of semi‐interpenetrating polymer networks based on polyurethane and polymethacrylate functional prepolymers

The motional transition and heterogeneity of semi‐interpenetrating networks (SIPNs) based on polyurethane (PU) with carboxylic groups and methacrylic copolymer (PM) with tertiary amine groups were studied by the electron spin resonance (ESR) spin probe method. The concentration of functional groups in both prepolymers varied from 0 to 0.45 mmol g^?1. Spin‐probed SIPNs show that the temperature‐dependent spectra are sensitive to polymer interactions imposed by functional groups. These interactions determine the free volume distribution in the matrix and temperature at which motional transition takes place. The fraction of free volume increases with functional group concentration and reaches its maximum at 0.25 mmol g^?1. Further increases in the functional group concentration reduce the free volume. The results of the networks with strong interactions are discussed in terms of the interference of the plasticizing effect of the PU component and the formation of possible cluster cross‐links, which restricts segmental motions. Copyright © 2003 Society of Chemical Industry     

Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives

Abstract. This paper describes artificial neural network based pure significance tests for the unit root hypothesis against nonlinear alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.     

WDM网络光层保护新算法的探讨

首先介绍了几种按照不同标准划分的WDM网络光层保护方法,然后提出了三种实现光层保护的新算法,对它们的内容和性能做了详细描述。按照提出的算法所求得的解,可以为WDM网络提供新的光层保护方式。     

一种实现最佳用户检测的非线性优化神经网络

本文提出并讨论了实现码分多址（CDMA）系统上最佳多用户检测（MUD）的一种神经网络方法。该方法通过将最佳多用户检测视为非线性优化组合问题，利用神经网络能有效求解非线性优化问题的优势，导出了一种非线性优化神经网络来实现最佳多用户检测，理论分析和计算机模拟表明，所提出的神经网络具有可实时应用的动态性能和较传统方法优越得多的误码率性能和抗多址干扰的性能。     

Boole函数的线性可分性(Ⅰ)——n-维超立方体的基本理论

Boole函数的线性可分性是前向人工神经网络理论中的一个比较困难的问题之一。目前仅对变量数n≤7的某些问题给予讨论。当n≥8时,尚无判别Boole函数线性可分的一般准则,更无线性可分Boole函数的计数公式。基于此,本文详细地研究了与Boole函数线性可分性有关的n-维超立方体的基本理论,包括n-维超立方体的基本性质、超立方体中的平行线、子超立方体的计数等,并给出了构造n-维超立方体图的一种新方法。     

Process monitoring using auto-associative,feed-forward artificial neural networks

The potential of using artificially simulated neural networks as intelligent, adaptive process-monitoring devices is discussed. The investigation is considered as a method for automatic, intelligent exception reporting for quality control applications. The technique is also compared with the conventional statistical approaches of principal component analysis and Kohonen's feature map. The applications of the technique in aerospace and manufacturing environments are presented and a possible extension of the method to incorporate a diagnostic function is discussed.Seconded from Cheltenham and Gloucester College of Higher Education as a Royal Society/SERC Research Fellow at Smith's Industries Aerospace and Defence Systems, Bishop's Cleeve, Cheltenham, UK.     

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。