Periodic boundary value problems for neutral multi-pantograph equations

In this paper, a new method is proposed for solving nonlinear neutral multi-pantograph equations with periodic boundary conditions by combining the advantages of the homotopy perturbed method (HPM) and the reproducing kernel method (RKM). The numerical results obtained show that the present method is reliable.     

A note on almost sure asymptotic stability of neutral stochastic delay differential equations with Markovian switching

In this paper, we consider neutral stochastic delay differential equations with Markovian switching. Our key aim is to establish LaSalle-type stability theorems for the underlying equations. The key techniques used in this paper are the method of Lyapunov functions and the convergence theorem of nonnegative semi-martingales. The key advantage of our new results lies in the fact that our results can be applied to more general non-autonomous equations.     

时滞中立型线性系统的变结构控制

本文研究了一类时滞中立型线性的变结构控制问题,在系统谱可控的条件下给出了具有稳定的油动模态的切换泛函的构造方法,并利用渐近率的概念和方法设计了该系统的变结构控制器,得到一般时滞中立型系统可变换成所需要的时滞中立型系统的条件。     

The improved stability analysis of the backward Euler method for neutral stochastic delay differential equations

The aim of this paper is to improve some results obtained in our earlier paper [Z. Yu and M. Liu, Almost surely asymptotic stability of numerical solutions for neutral stochastic delay differential equations, Discrete Dyn. Nat. Soc. 2011 (2011), article id 217672, 11 p., doi:10.1155/2011/217672]. In this paper, we establish an improved theorem and show that the backward Euler method can reproduce the property of almost sure and mean square exponential stability of exact solutions to neutral stochastic delay differential equations. To obtain the desired result, some new proof techniques are adopted.     

Weighted pseudo-almost automorphic solutions of high-order Hopfield neural networks with neutral distributed delays

In this paper, a class of high-order Hopfield neural networks with neutral distributed delays is considered. Some sufficient conditions are obtained for the existence, uniqueness and global exponential stability of weighted pseudo-almost automorphic solutions for this class of networks by employing the Banach fixed-point theorem and differential inequality techniques. The results of this paper are completely new. An example is given to show the effectiveness of the proposed method and results.

     

中立型时滞系统基于状态观测器的控制设计-LMI方法

研究一类中立型时滞系统基于状态观测器的反馈控制问题。目的是利用线性矩阵不等式(LMI)方法设计状态观测器和动态输出反馈控制器,使得相应的闭环系统渐近稳定。最后给出一个数值例子验证了本文所给结果的有效性。     

基于神经网络及系统辨识的舵机带宽测试

讲述了一种应用神经网络辨识算法测试舵机带宽的实用方法。文章简要介绍了舵机的工作特性和舵机模型的选取,概述了辨识算法的选取及实现过程,并在输入信号的选取和辨识数据的预处理等方面作了基本的探讨。实验仿真结果表明,基于线性神经网络的系统辨识具有很高的辨识速度和精度。     

Stability analysis of neutral systems with distributed delays

The stability of neutral systems with distributed delays is investigated in this paper. A modified Lyapunov-Krasovskii functional is constructed to study this class of systems. The proposed stability criterion is discrete-, distributed- and neutral-delay-dependent. In addition, by this method one can study the case when the coefficient matrix of the neutral delay term is time-varying uncertain. The reduced conservatism is illustrated in a numerical example.     

一类时变时滞中立微分方程的时滞相关稳定性判据

针对一类时变时滞中立型微分方程,利用Lyapunov--Krasovskii泛函方法,并结合线性矩阵不等式技巧,给出系统基于线性矩阵不等式（LMI）的全局渐近稳定的时滞相关充分条件,并通过算例与现有准则相比,本文所给条件是有效的具有较小的保守性。     

基于混合训练神经网络的武器系统效能评估

提出了一种将遗传算法和神经网络相结合的混合训练神经网络模型,分析了其基本思想方法、具体实现过程,以及在武器效能评估中的具体应用。实例分析表明,该模型用于武器系统效能评估可较好地克服人为因素和模糊随机性的影响,使评估结果更为合理可信。     

三电平逆变器基于d—q坐标系的SVPWM算法

本文分析了中点钳位（NPC）式三电平逆变器的工作原理，介绍了三电平逆变器基于d-q坐标系的控制算法。为解决在电压源型逆变器中的中点电位不平衡问题，提出了基于控制因子m的滞环比较法和基于控制因子ms0的准确计算法的控制策略，并采用Matlab的Simulink软件进行了系统仿真。仿真结果表明，该两种方法对于解决中点电位的平衡都有一定的效果。     

小规模情感数据和大规模中性数据相结合的情感韵律建模研究

建立好的情感韵律模型是合成情感语音的重要环节,而在情感语音的研究过程中,一个必须面对的现实问题就是通常情感数据量相比于中性数据量要少得多.将一个含有高兴、生气、悲伤3种情感语音的小规模数据库和一个较大规模的中性语音数据库相结合,进行情感韵律建模研究.对影响情感的韵律参数进行了分析,建立了基于人工神经网络的情感韵律模型.针对情感数据量相对于中性数据量的不足而导致的过拟合现象,提出了3种解决办法,即混合语料法、最小二乘融合法和级联网络法.这些方法都在不同程度上扩大了情感语料的作用,使得情感预测效果都有所提高.尤其是级联网络法,将中性模型的结果作为级联网络的一个输入,相当于扩大了情感模型的特征空间,更加强化了情感模型各输入特征的作用,在3种情感的各韵律参数生成中效果是最好的.     

Prosody conversion from neutral speech to emotional speech

Emotion is an important element in expressive speech synthesis. Unlike traditional discrete emotion simulations, this paper attempts to synthesize emotional speech by using "strong", "medium", and "weak" classifications. This paper tests different models, a linear modification model (LMM), a Gaussian mixture model (GMM), and a classification and regression tree (CART) model. The linear modification model makes direct modification of sentence F0 contours and syllabic durations from acoustic distributions of emotional speech, such as, F0 topline, F0 baseline, durations, and intensities. Further analysis shows that emotional speech is also related to stress and linguistic information. Unlike the linear modification method, the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech. While the GMM just uses the features, the CART model integrates linguistic features into the mapping. A pitch target model which is optimized to describe Mandarin F0 contours is also introduced. For all conversion methods, a deviation of perceived expressiveness (DPE) measure is created to evaluate the expressiveness of the output speech. The results show that the LMM gives the worst results among the three methods. The GMM method is more suitable for a small training set, while the CART method gives the better emotional speech output if trained with a large context-balanced corpus. The methods discussed in this paper indicate ways to generate emotional speech in speech synthesis. The objective and subjective evaluation processes are also analyzed. These results support the use of a neutral semantic content text in databases for emotional speech synthesis.     

A multi-domain Legendre spectral collocation method for nonlinear neutral equations with piecewise continuous argument

In this paper, for the neutral equations with piecewise continuous argument, we construct a spectral collocation method by combining the shifted Legendre–Gauss–Radau interpolation and a multi-domain division. Based on the non-classical Lipschitz condition, the convergence results of the method are derived. The results show that the method can arrive at high accuracy under the suitable conditions. Several numerical examples further illustrate the obtained theoretical results and the computational effectiveness of the method.     

Robust stabilitv of neutral stochastic interval svstems

Although interval systems have received a great deal of attention, so far there is few work on neutral stochastic interval systems (NSIS). The purpose of this paper is to initiate the study of NSIS. Using Raumikhin-type technique, a sufficient condition of exponential stability on NSIS is given. It is interesting that the NSIS changes into general stochastic interval system when the neutral item disappeared. So the results in this paper generalize some conclusions existed.     

On robustness of predictor feedback control of linear systems with input delays

This paper is concerned with the robustness of the predictor feedback control of linear systems with input delays. By applying certain equivalent transformations on the characteristic equation associated with the closed-loop system, we first transform the robustness problem of a predictor feedback control system into the stability problem of a neutral time-delay system containing an integral operator in the derivative. The range of the allowable input delay for this neutral time-delay system can be computed by exploring its delay dependent stability conditions. In particular, delay dependent stability conditions for the neutral time-delay system are established by partitioning the delay into segments. The conservatism of this method can be reduced when the number of segments in the partition is increased. Numerical examples are worked out to illustrate the effectiveness of the proposed method.     

基于BP神经网络的景象匹配算法

针对以可见光图像为基准、红外图像为实测的景象匹配问题,提出了一种基于BP神经网络的景象匹配方法。该方法首先抽取满足平移、旋转、尺度不变性的Krawtchouk不变矩作为红外目标的特征描述并构造特征向量,随后,基于主分量分析法消除特征向量各个分量间的相关性,去掉多个对目标匹配识别贡献不大的特征的影响,形成描述目标的有效特征向量。接下来,构造三层BP神经网络,以有效匹配特征作为输入,匹配位置作为输出,按照经验公式确定隐层节点个数,基于样本集对红外目标在可见光基准图像中的匹配过程进行训练,最终形成智能化景象匹配识别器。实验结果表明,与常用的景象匹配算法相比,提出方法不仅具有更高的匹配精度和速度,而且鲁棒性好,能抵抗实测图像的旋转几何畸变。     

基于权值优化神经网络的交通拥堵程度决策方法

针对现有的交通拥堵程度决策方法在证据不确定和不完备情况下评价准确率低的问题,提出了一种基于权值优化神经网络方法。首先,采用二次型隶属度描述了各类交通拥堵证据的不确定性与不完备性。其次,由信息熵计算证据的贡献度,并作为优化神经网络的输入层权值。接下来,由初始权值与前序时刻的梯度和自适应更新各隐层与输出层的神经元权值,以期降低不确定和不完备证据给整个网络带来的累积误差。最后,结合实际交通状况进行算例分析,验证了该方法的准确性与收敛性。结论分析表明,所提出的方法能作出准确的交通拥堵程度决策。     

A New Delay‐Dependent Stability Criterion For Neutral Delay‐Differential Systems

This paper investigates the problem of asymptotic stability for neutral delay‐differential systems. Using the Lyapunov method, we derive a new delay‐dependent sufficient condition for the stability of systems in terms of the linear matrix inequality (LMI). Numerical examples show that the results obtained in this paper significantly improve the estimate of stability limit over some existing results reported previously in the literature.     

JDBC与ODBC在网络数据库开发中的应用研究

该文研究了ODBC和JDBC的技术支持和应用方法,提出了利用Java语言的体系结构无关性和动态性设计Browser／Server模式系统,实现跨平台、兼容多种数据库的网络信息系统开发思想.