A circuit technique for broadbanding the electronic tuning range of Gunn oscillators

A circuit technique is described whereby the electronic tuning range obtained by varactor tuning solid-state oscillators, such as Gunn oscillators, can be improved. The principle of the technique has been demonstrated by doubling the tuning range obtained from a coaxial X-band Gunn oscillator using distributed circuit elements. An analytical expression for the improved tuning range is presented and predictions for the improvement in an existing microstrip X-band oscillator using chip devices given.     

Simulation of dynamics of the neural ensemble in an active wireless network

The problem of emulation of the Hopfield networks in active wireless networks is considered. A network with six elements communicating via radio channels is studied experimentally.     

动力学神经网络模型的构建及其稳定性研究

针对静态人工神经网络具有在反映系统动态行为时网络结构复杂、不能很好地反映系统动态性能的缺点,提出一种由带有积分器和可调反馈系数的神经元构成的新型动力学神经网络模型。该网络比以前的动态网络即递归网络或在此基础上改进的网络能更好地反映系统的动态性能,网络的结构更加简单,训练过程加快,从而使系统能够更好的运行。利用梯度下降法研究了该网络的权值调整算法,并通过李雅普诺夫稳定性判据讨论了这种新型动力学神经网络的稳定性条件。该网络研究为反映系统的动力学行为提供了更好的模型结构和理论算法,为神经网络的发展提供了新的研究方向。     

基于BP神经网络的模塑封电子器件优化设计

针对塑封SOT(小外形晶体菅)器件的使用失效案例,从芯片设计角度出发,提出一种优化设计方法,该方法利用误差反向传播神经网络(BPNN),结合主成分分析(PCA)、遗传算法(GAs)及均匀设计的针对非线性系统的优化设计,设计了该塑封SOT器件的尺寸参数。结合实验和有限元模拟分析,验证该优化设计结果的有效性。结果表明,优化设计的器件各关键界面点的最大应力约减少了70~180MPa,器件的界面层裂现象得到消除,提高了器件的可靠性。     

A stochastic network model of the interaction between cardiacrhythm and artificial pacemaker

In order to study heart-pacemaker interaction (HPI), a computer model of the cardiac conduction system has been developed which includes the effects of artificial pacemaker function and failure. The stochastic network model of cardiac conduction consists of five vertices, each representing a functional electrophysiologic element. Electrophysiologic multidimensional conditional probability functions determine the depolarization status of each vertex. The atrioventricular (AV) node is emulated using a mathematical model which includes the influence of past cycle lengths on AV nodal conduction time. Twenty-three classes of arrhythmias may be simulated and, for pacing simulation, one of 12 antibradycardia pacing modes may be chosen. Random effects of pacemaker malfunction including oversensing, undersensing, or failure-to-capture may be simulated through the use of probability distribution functions. This model should prove useful in the development of pacemaker algorithms, determining patient-specific pacemaker therapy, and predicting causes for apparent pacemaker malfunction. The model has been used in the development of an expert system to analyze paced ECGs for pacemaker function and malfunction     

防空兵群火力单位机动部署的Hopfield神经网络模型研究

针对防空兵群火力单位机动部署问题,引入了基于Hopfield神经网络的解决方法．建立了神经网络模型,给出了神经网络能量函数的表示方法及状态方程,并在Matlab中进行了仿真实验,实验表明该方法是可行的。     

A neural network compensator for uncertainties of roboticsmanipulators

A neural network controller for trajectory control of robotic manipulators that is used not to internalize the inverse dynamic model of the controlled object but to compensate only the uncertainties of the robotic manipulator is presented. Its performance is compared with that of the conventional adaptive scheme. The results show the ability of the neural network controller to adapt to unstructured effects. A learning method for the neural network compensator with true teaching signals is shown. The tracking error of the robotic manipulator was greatly reduced when this controller was used     

基于神经网络的雷达抗干扰方法综述

针对目前雷达抗干扰方法存在的局限性,提出了将神经网络技术应用于雷达抗干扰的研究思路。首先介绍了在雷达抗干扰领域应用比较成功的3种神经网络模型:BP网络、RBF网络和Kohonen网络;然后概括了神经网络应用于雷达抗干扰的3种主要途径:神经网络用于建模、用于特征提取和用于分类;最后对下一步的研究进行了总结与展望,分析了神经网络与其他技术相结合在雷达抗干扰中的应用前景。     

Experimental studies on the electronic and mechanical tuning properties of resonant-cap IMPATT oscillators

A detailed experimental investigation has been made on the electronic tuning of resonant-cap IMPATT oscillators at the X-band. The investigation is extended to include mechanical tuning properties, i.e. studies on the dependence of the tuning properties on the resonant-cap diameter and the cap height. The study of electronic tuning gives an interesting insight regarding tuning behaviour for various ranges of d.c. bias current. From the study of these tuning properties the optimum size of the resonant-cap cavity for realizing maximum power output and electronic tunability for a given IMPATT diode can be obtained, which would be useful in the design of resonant-cap IMPATT oscillators.     

Hopfield神经网络的存贮容量

我们把理论推导与数值模拟相结合得出一个较好的误差函数近似解析式。应用该解析式分析了Ｈｏｐｆｉｅｌｄ神经网络绝对存同容量，得到了一更严格的结果。     

一种基于组合神经网络的HEVC帧间预测方法

高效视频编码(HEVC)作为最新视频编码标准,有着非常高的压缩效率,但是由于各种新技术的提出,其编码复杂度也大大提高。复杂度对视频编码有着重要意义,低复杂度编码的研究非常必要。利用神经网络进行HEVC的分区预测为低复杂度编码提供了有效的解决方案。文中提出了一种基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的组合网络架构来对帧间分区进行预测的方法,利用自建数据库对网络进行训练;文中设计了一种预搜索模块来建立训练数据库,仿真结果表明,神经网络的精度可达87%,利用该网络架构进行帧间预测可以实现52%~71%的复杂度节省。     

一种基于神经网络的目标优先级确定方法

传感器管理的目的之一是将多传感器资源分配给多目标。分配时需要考虑的因素之一是目标的优先级别。本文分析了影响目标优先级的主要因素,提出了一种基于人工神经网络的目标优先级确定算法。仿真结果表明,该算法是精确而有效的。     

A quick neural network for computer vision of gray images

This paper presents a comparison between the traditional image processing method and the area vector concept as well as the new technique of artificial neural networks. Freeman chain coding is considered in the study, and the principle of segmentation may be based and implemented for further investigations resulting from the proposed work. The pattern recognition concept is analyzed and defined through the sigmoid function and the determination of the threshold of a gray image for an object. The block schemes for the given protocols are summarized in a single scheme for illustration and comparison purposes. The synthetic pictures are generated and investigated regarding the dependence of computer vision on the contents of the artificial neural network. The normalization technique is included to eliminate noise and zooming problems. The minimum computational time for image processing with the generated pictures is also determined. The rate of deflection in the computational time is recommended for sensing the minimum computational time according to the variation of the number of hidden units in the hidden layer. A three-layer neural network has been used. The study of gray binary imaging for color pictures is illustrated to save computational time and effort.     

Influence of the model for random birefringence on the differential group delay of periodically spun fibers

We consider the two Wai-Menyuk models of birefringence in periodically spun fibers, and we show that the differential group delay differs significantly for the two models when the spin period approaches or exceeds the fiber beat length. When the fiber correlation length is large, we explain this difference quantitatively, and we explain it qualitatively for any fiber correlation length.     

神经网络中隐含层的确定

主要通过PLD算法确定神经网络中隐含层神经元的数目及连接权值,并通过Matlab随机模拟生成一个二维三类线性可分集,用传统的BP网络和本文提出的PLD算法分别对其进行分类实验.     

A methodology for neural network training for control of driveswith nonlinearities

The learning process of a multilayered feedforward neural network involves extracting a desired function from the training data presented through an appropriate training algorithm. To achieve the desired function, the generation of good training data is necessary. A closed-loop methodology for neural network training for control of drives with nonlinearities is presented. Problems associated with the more common open-loop training scheme, and how these are addressed by the proposed closed-loop method, are discussed. An inverse nonlinear control using a neural network (INC/NN), a control strategy which incorporates the neural network for control of nonlinear systems, is described and used to demonstrate the effectiveness of the closed-loop training scheme. Simulation studies and experimental results are presented to verify the improvement achieved by the closed-loop training methodology     

云桌面虚拟化电子产业云物流环境下灰色神经网络模型预测应用探讨

基于准确的云桌面虚拟化云物流需求预测对璧山县的云物流管理活动具有重要的指导意义。运用灰色关联度与灰色神经网络模型,对璧山电子产品云桌面虚拟化云物流需求进行动态预测,预测结果的检验与分析,表明模型较精确,可以为璧山县电子产品云桌面虚拟化云物流需求预测多层进程预测。     

A new multi-valued neural network for the extraction of lumped models of analog circuits

A novel identification technique for lumped models of general distributed circuits (i.e. microwave transmission lines, monolithic integrated circuits and filters) is presented. The approach is based on a hybrid multi-valued neuron neural network with a modified layer and learning process, whose convergence allows the validation of the approximated lumped model. The modified layer is generated by symbolic analysis of the model under exam. The inputs of the neural network are geometrical parameters, while the outputs represent the estimation of the lumped circuit parameters.     

A neural network approach to the traffic control problem in reverse baseline networks

A neural network for the traffic control problem applied to reverse baseline networks has been proposed in this paper. This problem has been first represented by an energy function. A neural network is applied for maximizing the energy of the function under the constraints of the reverse baseline network. The number of iteration steps in our neural network is limited by a performed upper bound O(n), wheren is the size of ann ×n network. The throughputs of our neural network have been shown by the empirical results to be better than the conventional algorithm (modified Bipartite Matching Algorithm) when the packet densities rise higher than 50%.