智能光网络控制平面技术

光网络的智能化是下一代光网络发展的主要趋势,在对智能光网络发展背景进行分析的基础上,对光网络控制平面技术进行了讨论,重点分析了光网络控制平面的功能模型、主要功能、应用协议和控制平面的可靠性等问题。     

改进型遗传算法在加载天线设计中的运用

遗传算法是一种全新的优化搜索方法，可以用来解决各种复杂的实际问题。针对简单型遗传算法的一些不足之处，介绍了一种改进型的遗传算法，并阐述了其在短波宽带集总加载天线优化设计中的运用。     

射频板条CO_2激光器并联谐振技术的研究

本文利用周期性网络模型计算了射频板条CO2激光器电极的纵向电压分布．探讨了并联谐振技术在板条器件中获得成功运用的原因．提出了利用并联谐振技术进一步提高电压分布均匀性的两个途径．     

遗传算法在期货市场创新决策中的应用

研究了遗传算法在期货市场创新决策中的应用问题，在期货市场的保值者和投资者数目以及期货合约结构模型的基础上，设计了期货市场创新的染色体表达和遗传操作并进行了仿真研究。     

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。     

无线局域网安全问题研究

为了提供相当于有线局域网的数据安全,IEEE802.11定义了有线等价保密(WEP)协议。然而,最近的研究发现WEP存在严重的缺陷。介绍了无线局域网存在的安全隐患,分析了WEP的结构以及WEP协议的缺陷以及可能遭受的攻击,并探讨了改进方案。     

给水管网计算中的数据转换方法

在给水管网计算中,通过建立环-管段矩阵、环-节点矩阵以及输入节点流量、节点坐标等少量数据,通过计算和数据转换,便可求得经济管径、管段设计流量、节点水压值、水厂供水量、管网造价、年费用折算值等。采用该方法进行管网计算,可以减少40%～70%的计算时间。     

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.