宽带自适应跳频控制器

本文叙述宽带自适应跳频控制器的原理及方案,频率集的配置、自适应跳频拒收门限及频点更新、跳频码序列及同步等。有效地组织宽带自适应跳频系统能避开大部分固定和半固定性质的单频连续波干扰,使得移动卫星通信系统中实现高可靠性的数据传输。     

心电地图仪中工频干扰的一种滤除方法

在心电地图仪中记录到的体表心电信号往往由于电磁的影响而引进工频干扰．基于体表心电信号中工频干扰的特点，作者提出了一种滤除工频干扰的方法，即就单独—胸导联信号通过提高频谱的分辨率来估计工频干扰的频率；基于最小均方误差准则来估计各导联信号中工频干扰的幅度和相位。文中还给出了该算法的流程图和滤波性能分析。模拟结果表明了该算法的有效性。应用该算法对心电地图仪中的工频干扰进行对消，取得了满意的结果。     

给水流量控制系统仿真辅助设计

本文将给水流量系统规范化，给出静、动态数学模型，通过计算机仿真计算建立起设计参数Ｓ与系统特性间关系数据库，这些数据可用于控制系统辅助设计。本文还介绍了在Ｓ较小时自动补偿系统增益和时间常数的自适应控制方法并给出仿真结果。     

Neural networks for nonlinear state estimation

Estimating the state of a nonlinear stochastic system (observed through a nonlinear noisy measurement channel) has been the goal of considerable research to solve both filtering and control problems. In this paper, an original approach to the solution of the optimal state estimation problem by means of neural networks is proposed, which consists in constraining the state estimator to take on the structure of a multilayer feedforward network. Both non-recursive and recursive estimation schemes are considered, which enable one to reduce the original functional problem to a nonlinear programming one. As this reduction entails approximations for the optimal estimation strategy, quantitative results on the accuracy of such approximations are reported. Simulation results confirm the effectiveness of the proposed method.     

存在噪声和未建模动态系统传递函数的渐近无偏估计

存在未建模动态时，如何去除系统传递函数估计中噪声引起的偏差是系统辨识关心的重要问题。本文将我们已提出的ＢＥＬＳ方法进行推广得出：当系统未建模动态可用－ＦＩＲ模型描述时，应用本文的方法可得到系统传递函数的无偏辨识，而不需知道有关噪声和未建模动态概率分布函数的先验知识。     

关于可分2－D系统的几个注记

可分２－Ｄ系统是一类具有良好特性的重要的特殊２－Ｄ系统，它在多方面均有重要的应用．本文在已有结果的基础上对具有可分性的２－Ｄ多输入多输出系统在再实现问题、渐近稳定性代数判据、状态观测器设计等方面进行了较为广泛的研究，得到了许多较好的结果．     

固相缩聚共聚酯的熔融行为和结晶速率研究

通过固相缩聚合成了两种不同结构的高分子量共聚酯，研究了样品的熔融行为和结晶速率。研究发现，共聚酯的熔融峰随着固相聚合温度和时间的变化而与纯ＰＥＴ有明显的差别，共聚酯泊结晶速率与慢于纯ＰＥＴ的结晶速率。     

红外热图技术在气功研究中的应用

本文用红外技术研究发功时上肢体表温度变化的特点和它的规律。     

纳米级BaAl_2O_4微粉的制备和性质

分别用溶胶 -凝胶法、低温固相反应法 ,制得了纳米级BaAl2 O4微粉 ,用电镜观察了其形貌和粒径的大小 ,用X-射线衍射仪进行了物相分析 ,测定了其纯度与晶型 ,讨论了制备方法与粒度大小的相互影响     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.