An efficient fractional-order wavelet method for fractional Volterra integro-differential equations

In this paper, an efficient and robust numerical technique is suggested to solve fractional Volterra integro-differential equations (FVIDEs). The proposed method is mainly based on the generalized fractional-order Legendre wavelets (GFLWs), their operational matrices and the Collocation method. The main advantage of the proposed method is that, by using the GFLWs basis, it can provide more efficient and accurate solution for FVIDEs in compare to integer-order wavelet basis. A comparison between the achieved results confirms accuracy and superiority of the proposed GFLWs method for solving FVIDEs. Error analysis and convergence of the GFLWs basis is provided.     

The variable step-size LMS/F algorithm using nonparametric method for adaptive system identification

A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real-time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step-size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step-size equation, this article proposes the nonparametric variable step-size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real-time setup validate that the proposed NPVSS-LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise.     

A novel method for fault diagnosis of hydro generator based on NOFRFs

Fault diagnosis and recognition of hydro generator are important issues which encounter in repair and security posture assessment. It influences the operational planning and security directly. Volterra series as an effective modeling method has been widely used in modeling and fault diagnosis of hydro generator, but the larger number of kernels limits its application in faults diagnosis. Non-linear output frequency response functions (NOFRFs) as a transformation style of Volterra series have a more intuitive visual and simple structure. In this paper, NOFRFs have been proposed to be employed in fault diagnosis of hydro generator, and a novel online identification method is proposed at the same time. Firstly, NOFRFs models of hydro generators are built. Secondly, a new method for online identification is proposed according to the operational characteristics of hydro generators. Finally, simulation verification has been done to demonstrate the advantage of the proposed method, and experimental studies are put forward on a hydro generator to analyze the failure mechanism in different states. The results indicate that the proposed method is useful and concise for fault diagnosis and nonlinear analysis of hydro generators.     

Theoretical basis for extraction of mildly nonlinear behavioral models

The analytical and laboratory requirements for the extraction of a mildly nonlinear behavioral model are derived. This shows that they greatly exceed current capabilities in general, and a simplified procedure based on dynamic linear element deembedding is proposed. This procedure is then illustrated for a case of practical significance by discussing various methods to extract an intermodulation model of a MES field effect transistor. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 40–53, 2003.     

一种全解耦的RLS自适应Volterra滤波器

本文研究了Volterra自适应滤波器解耦问题,提出了一种全解耦的RLS自适应Volterra滤波器.按照Volterra滤波器的伪线性组合结构,采用RLS滤波原理和约束优化理论,导出了满足最小累计平方误差指标的具有分块对角形输入相关矩阵的全解耦Volterra标准方程,据此设计了一种全解耦的RLS自适应Volterra滤波器,给出了滤波器权向量自适应修正的一套公式.仿真结果验证了本文方法的有效性.     

Echo canceler algorithm for adaptive Volterra filters in double talk states

In an acoustic echo canceler, it is necessary to consider the nonlinearity of an echo path in order to attenuate an echo signal conventionally, and various methods using adaptive Volterra filters have been proposed. Many of those methods have not resulted in a decisive solution about the double talk states in a nonlinear acoustic echo canceler. In this paper, we propose an echo canceler algorithm for adaptive Volterra filters using the idea of the OECLMS (Orthogonal Expanded Correlation Least Mean Squares) algorithm in consideration of the double talk problem. The convergence speed of the proposed method is improved by using Gram‐Schmidt orthogonalization for the input signal. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(2): 42– 51, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20369     

Nyquist stability criterion and positive-realness of sampled-data systems

In this paper, we first derive a Nyquist stability criterion of sampled-data systems in such a form that reflects the treatment of sampled-data systems with their intersample behavior taken into account. We next analyze the positive-realness of sampled-data systems with D₁₁ matrix (i.e., the continuous-time direct feedthrough matrix from the disturbance input w to the controlled output z) being 0, and show that such sampled-data systems cannot be made positive-real, unless the relative degree of the system from w to z is 1. We further show that adding some appropriate D₁₁ leads to positive-real sampled-data systems, and give a method to find such D₁₁ that is as small as possible. This should be useful for the stability analysis of sampled-data systems with nonlinearities. All these analyses, as well as the small-gain analysis, are compared from the viewpoint of the gain margin analysis.     

一种改进的Volterra自适应噪声对消器

针对随着Voherra滤波器的输入维数或记忆单元增大,相应需要的计算复杂性成幂级数快速增加的问题,提出了一种改进的利用sigmoid函数对背景噪声进行预处理的减少参数Voherra自适应滤波器,并用它构造了非线性自适应噪声对消器。仿真结果表明,算法在高斯噪声和α稳定分布噪声环境下都有优越的抗噪性能。     

基于Volterra级数预测的音频频带扩展

 本文采用非线性分析方法,基于Volterra级数提出了一种宽带音频信号的频带扩展方法,并利用高斯混合模型(Gaussian Mixture Model,GMM)和码本映射技术对扩展后的音频信号进行了谱包络和能量增益调整.实验表明,所提算法的性能要好于已有的非线性频带扩展算法,当用本文的方法替代ITU-T G.722.1C编码器中的噪声填充技术时,在24kbps得到了提升的超宽带音频质量.