网络电视的发展及其对远程教育的影响

计算机网络技术的发展给电视节目的播出方式带来了巨大的变化，网络电视这一传播媒体应运而生。从而改变了目前广播电视媒体信息单向传播的缺陷。介绍了网络电视的发展，就网络电视对远程教育的影响进行了探讨。     

支持向量机在交通量预测中的应用

交通量预测对于区域交通规划有重要意义。提出一种基于支持向量机理论的交通量预测方法。该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

自适应有源噪声控制的滤波-LMAD算法

本文研究了稳定非高斯环境下的自适应有源噪声控制问题，提出了滤波－ＬＭＡＤ（ｌｅａｓｔｍｅａｎａｂｓｏｌｕｔｅｄｅｖｉａｔｉｏｎ）算法及归一化滤波－ＬＭＡＤ算法，仿真结果表明，与常用的滤波－ＬＭＳ算法和归－化滤波－ＬＭＳ算法相比，本文提出了算法收敛速度更快。     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Active control of extended Van der Pol equation

We study in this paper the active control of a driven class of Van der Pol oscillator which exhibits three limit cycles. We begin by investigating the dynamics and stability analysis of the system under active control. We also analyze the effects of a time periodic perturbation included in the control process. In all these cases the domain of control gain parameters leading to a good control is obtained and verified numerically.     

聚异丁烯高活性端基含量及相对分子质量测定方法的研究

综合使用核磁，VPO和近红外光谱分析技术对聚异丁烯活性端基含量以及相对分子质量的测定进行了详细研究，分别建立核测定聚异丁烯活性端基含量以及其他烯键含量和近红外光谱快速测量聚异聚丁烯活性端基含量。其他烯键含量和相对分子质量的分析方法。成对t检验结果表明，近红外光谱分析方法测定结果与核磁和VPO方法测定结果之间无显著性差异。     

A 24-GHz Patch Array with a Power Amplifier/Low-Noise Amplifier MMIC

This paper presents an active patch array designed at 24 GHz. It can be used as a front-end component for a phased array. A series resonant array structure is chosen which is compact and easy excite. With 5 elements, the array proved a 12-dB antenna gain. A power amplifier and a low noise amplifier are designed on a single GaAs chip (PALNA). Bias switch is used in the PALNA, which greatly reduces the switch loss in a transceiver and increases the efficiency. 20-dB small signal gain is achieved in both power amplifier and low noise amplifier. The active patch array is built by the combination of the patch array and PALNA. The measured active gain of this antenna is 35-dB for the PA mode and 31-dB for the LNA mode. This active patch array can obtain an EIRP of 34 dBm with a total radiated power of 22dBm and a maximum PAE of 32%. To check the noise performance, we applied sources at both normal temperature and 77K (liquid nitrogen) and extracted the noise figure (3.5 dB) of the active antenna by the Y factor method. The results proved that the active antenna is working efficiently as both a transmitting and receiving antenna.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.