A Method for Decision Making Based on Generalized Aggregation Operators

A new method for decision making based on generalized aggregation operators is presented. We use a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index uses distance measures and other techniques that are very useful for decision making. In this paper, it is suggested a generalization by using generalized and quasi‐arithmetic means. As a result, it is obtained the generalized and quasi‐arithmetic weighted IMAM (GWIMAM and quasi‐WIMAM) and the generalized ordered weighted averaging IMAM (GOWAIMAM) and the quasi‐OWAIMAM operator. The main advantage is that it provides a parameterized family of aggregation operators that includes a wide range of special cases such as the generalized IMAM and the OWAIMAM. Thus, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. We also develop an application of the new approach in a decision‐making problem regarding product selection.     

The uncertain induced quasi‐arithmetic OWA operator

We present the uncertain induced quasi‐arithmetic OWA (Quasi‐UIOWA) operator. It is an extension of the OWA operator that uses the main characteristics of the induced OWA (IOWA), the quasi‐arithmetic OWA (Quasi‐OWA) and the uncertain OWA (UOWA) operator. Thus, this generalization uses quasi‐arithmetic means, order inducing variables in the reordering process and uncertain information represented by interval numbers. A key feature of the Quasi‐UIOWA operator is that it generalizes a wide range of aggregation operators such as the uncertain quasi‐arithmetic mean, the uncertain weighted quasi‐arithmetic mean, the UOWA, the uncertain weighted generalized mean, the uncertain induced generalized OWA (UIGOWA), the Quasi‐UOWA, the uncertain IOWA, the uncertain induced ordered weighted geometric (UIOWG), and the uncertain induced ordered weighted quadratic averaging (UIOWQA) operator. We study some of the main properties of this approach including how to obtain a wide range of particular cases. We further generalize the Quasi‐UIOWA operator by using discrete Choquet integrals. We end the article with an application of the new approach in a decision making problem about investment selection. © 2010 Wiley Periodicals, Inc.     

一种直扩信号的检测方法

在非协作通信中,基于循环谱理论的检测方法是直扩信号检测的重要方法之一.目前,该方法都是假设背景噪声和干扰为平稳信号.但是实际上,直扩信号面临严重的窄带干扰,特别是在军事领域,更是面临敌方有意的人为强窄带干扰.由于存在调制,人为的窄带调制信号都存在循环平稳特性,这对目前基于循环谱理论的直扩信号检测方法提出了挑战.文中通过理论分析、MATLAB仿真,分析了直扩信号、窄带调幅干扰信号,验证了窄带调幅信号具有循环平稳特性,提出了利用循环谱包络检测存在窄带调幅干扰的直扩信号检测方法,并写出了具体的算法.     

The effect of noise on adaptive envelope-constrained filters

The performance of the envelope-constrained filters designed by the newly developed adaptive algorithms may deteriorate when the input signal is contaminated by noise. The average window with a growing window size, and the exponentially weighted average window are presented. It is shown that the noise-induced bias in the augmented cost function will be reduced because applying such an averaging scheme helps increase values of the signal-to-noise ratio at the filter input channel. With this, the noise effect on the adaptive envelope-constrained filters can be depressed. Simulation results are given to illustrate the improved performance of the adaptive envelope-constrained filters designed in conjunction with the average window.     

Study on the ranking problems in multiple attribute decision making based on interval‐valued intuitionistic fuzzy numbers

This paper puts forward a new ranking method for multiple attribute decision‐making problems based on interval‐valued intuitionistic fuzzy set (IIFS) theory. First, the composed ordered weighted arithmetic averaging operator and composed ordered weighted geometric averaging operator are extended to the IIFSs in which they are, respectively, named interval‐valued intuitionistic fuzzy composed ordered weighted arithmetic averaging (IIFCOWA) operator and interval‐valued intuitionistic composed ordered weighted geometric averaging (IIFCOWG) operator. Afterwards, to compare interval‐valued intuitionistic fuzzy numbers, we define the concepts of the maximum, the minimum, and ranking function. Some properties associated with the concepts are investigated. Using the IIFCOWA or IIFCOWG operator, we establish the detailed steps of ranking alternatives (or attributes) in multiple attribute decision making. Finally, an illustrative example is provided to show that the proposed ranking method is feasible in multiple attribute decision making.     

Hesitant Fuzzy Linguistic Maclaurin Symmetric Mean Operators and their Applications to Multi‐Criteria Decision‐Making Problem

Due to the limitation of knowledge and the vagueness of human being thinking, decision makers prefer to use hesitant fuzzy linguistic sets (HFLSs) to estimate alternatives. Some methods of HFLSs have been researched based on the more familiar means such as the arithmetic mean and the geometric mean; however, Maclaurin symmetric mean (MSM) that can be used to reflect the interrelationships among input arguments have not been applied to solve hesitant fuzzy linguistic multi‐criteria decision‐making problems. In this paper, two hesitant fuzzy linguistic harmonic averaging operators are proposed: the hesitant fuzzy linguistic MSM (HFLMSM) operator and the hesitant fuzzy linguistic weighted MSM (HFLWMSM) operator. Furthermore, an approach based on the HFLWMSM operator is proposed. Finally, to verify the validity and feasibility of the proposed approach, an illustrative example and corresponding comparison analysis are presented in the end.     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

Robust MIMO disturbance observer analysis and design with application to active car steering

A multi‐input–multi‐output extension of the well‐known two control degrees‐of‐freedom disturbance observer architecture that decouples the problem into single‐input–single‐output disturbance observer loops is presented in this paper. Robust design based on mapping D‐stability and the frequency domain specifications of weighted sensitivity minimization and phase margin bound to a chosen controller parameter space is presented as a part of the proposed design approach. The effect of the choice of disturbance observer Q filter on performance is explained with a numerical example. This is followed by the use of structured singular values in the robustness analysis of disturbance observer controlled systems subject to structured, real parametric and mixed uncertainty in the plant. A design and simulation study based on a four wheel active car steering control example is used to illustrate the methods presented in the paper. Copyright © 2009 John Wiley & Sons, Ltd.     

An improved method for on-line averaging and detecting of ECG waveforms

The most widely used methods for accurate signal averaging were studied and compared in order to gain a better understanding of the qualities and performances of each method. The level-triggering, contour-limiting, and correlation methods were simulated and compared. A new correlation method which is a weighted correlation of differences proved to be most suitable for real-time signal averaging, and detection of waveforms' variations. Simulated ECG waveforms and real ECG recordings were analyzed in this study. Twenty-eight ECG recordings of unipolar leads for noninvasive detection of the His-Purkinje activity were averaged separately by each method. The success in detection and the signal to noise ratio of the detected His activity obtained by each method was compared. Simulated ECG waveforms with random noise added were analyzed by four methods and the correct alignment as a function of the noise level was measured. The performance of our method in rejection of noisy waveforms and in detection of small variations in the waveforms is demonstrated.     

A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments

In this paper, we propose a speech enhancement method where the front-end decomposition of the input speech is performed by temporally processing using a filterbank. The proposed method incorporates a perceptually motivated stationary wavelet packet filterbank (PM-SWPFB) and an improved spectral over-subtraction (I-SOS) algorithm for the enhancement of speech in various noise environments. The stationary wavelet packet transform (SWPT) is a shift invariant transform. The PM-SWPFB is obtained by selecting the stationary wavelet packet tree in such a manner that it matches closely the non-linear resolution of the critical band structure of the psychoacoustic model. After the decomposition of the input speech, the I-SOS algorithm is applied in each subband, separately for the estimation of speech. The I-SOS uses a continuous noise estimation approach and estimate noise power from each subband without the need of explicit speech silence detection. The subband noise power is estimated and updated by adaptively smoothing the noisy signal power. The smoothing parameter in each subband is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement method is tested on speech signals degraded by various real-world noises. Using objective speech quality measures (SNR, segmental SNR (SegSNR), perceptual evaluation of speech quality (PESQ) score), and spectrograms with informal listening tests, we show that the proposed speech enhancement method outperforms than the spectral subtractive-type algorithms and improves quality and intelligibility of the enhanced speech.     

Speech enhancement using a Bayesian evidence approach

We consider the enhancement of speech corrupted by additive white Gaussian noise. In a Bayesian inference framework, maximum a posteriori (MAP) estimation of the signal is performed, along the lines developed by Lim & Oppenheim (1978). The speech enhancement problem is treated as a signal estimation problem, whose aim is to obtain a MAP estimate of the clean speech signal, given the noisy observations. The novelty of our approach, over previously reported work, is that we relate the variance of the additive noise and the gain of the autoregressive (AR) process to hyperparameters in a hierarchical Bayesian framework. These hyperparameters are computed from the noisy speech data to maximize the denominator in Bayes formula, also known as the evidence. The resulting Bayesian scheme is capable of performing speech enhancement from the noisy data without the need for silence detection. Experimental results are presented for stationary and slowly varying additive white Gaussian noise. The Bayesian scheme is also compared to the Lim and Oppenheim system, and the spectral subtraction method.     

基于加权子空间投影的谱估计器分辨力研究

针对常规子空间类算法在低信噪比、小快拍数情况下分辨力差的问题,分别对信号和噪声子空间提出加权投影算法来加以改善。对于信号子空间,采用主特征值与噪声功率之差的倒数对其特征向量加权;对于噪声子空间,将导向矢量在噪声子空间正交基各元素上的投影值作为权值,对正交基各元素加权。仿真实验表明,这两种算法能有效降低信源分辨的信噪比和快拍数门限,在低信噪比与小快拍条件下具有较好的分辨力和测量精度。     

基于递推最小二乘滤波器的语音增强

根据语音信号短时预测特性和短时准周期性，以当前采样前若干点和前若干周期的采样为观测样本，采用横向滤波器结构，构造基于递推最小二乘算法的自适应滤波器。该算法无需先验知识和参考信道，且对平稳或非平稳噪声均适用。实验证明，信噪比有明显提高，且增强后语音信号LPC倒谱与纯净语音LPC倒谱的欧氏距离也比增强前有了明显减小。     

Theoretical analysis of an information-based quality measure for image fusion

While recently a few image fusion quality measures have been proposed, analytical studies of these measures have been lacking. Here, we focus on one popular mutual information-based quality measure and weighted averaging image fusion. Based on an image formation model, we obtain a closed-form expression for the quality measure and mathematically analyze its properties under different types of image distortion. Tests with real images are also presented which agree with the conclusions of the analytical results. The results show the quality measure studied does not generally properly characterize increases in the distortion (noise and blurring) of the images which are input into a weighted averaging fusion algorithm.     

Induced Heavy Moving Averages

This paper presents the induced heavy ordered weighted moving average (IHOWMA) operator. It is an aggregation operator that uses the main characteristics of three well‐known techniques: the moving average, induced operator, and heavy aggregation operator. This operator provides a parameterized family of aggregation operators that include the minimum, the maximum, and total operator as special cases. It can be used in a selection process, considering that not all decision makers have the same knowledge and expectations of the future. The main properties of this operator are studied including a wide range of families of IHOWMA operators, such as the heavy ordered weighted moving average operator and uncertain induced heavy ordered weighted moving average operator. The IHOWMA operator is also extended using generalized and quasi‐arithmetic means. An example in an investment selection process is also presented.     

On two weighted signal averaging methods and their application to the surface detection of cardiac micropotentials

In this paper, the variance normalized averaging (VNA) and the optimal weighted averaging (OWA) are derived and their application to the surface detection of cardiac micropotentials is discussed. Theoretical analysis and computer simulation showed that VNA and OWA are superior to the conventional signal averaging (CSA) in reducing random noise with changing variance and the larger the change of noise variance the better the improvements of VNA and OWA relative to CSA. Clinical application of VNA and OWA using a proposed noise variance estimation technique indicated that residual noise on the PR and ST segments can be further reduced in most of the cases. This manifests that the new techniques have a potential advantage for improving the effectiveness of signal averaging as a fundamental method for surface detection of cardiac micropotentials.     

动态双线性工业过程稳态模型的强一致估计

针对双线性动态工业过程,提出一种辨识其稳态模型的方法.该方法利用阶跃信号作为辨识输入信号和过程稳态输出采样值的滑动平均近似稳态输出值,从而构造一种线性矩阵关系,获得其未知系数矩阵的估计.该过程在阶跃输入情况下是一个严格稳定的过程,所以其稳态输出采样值是拟平稳的,从而这种估计是强一致的.数字仿真充分证明了该方法是有效和实用的.     

Pythagorean Dombi fuzzy aggregation operators with application in multicriteria decision-making

A Pythagorean fuzzy set, an extension of intuitionistic fuzzy sets, is very helpful in representing vague information that occurs in real world scenarios. The Dombi operators with operational parameters, have excellent flexibility. Due to the flexible nature of these Dombi operational parameters, this research paper introduces some new aggregation operators under Pythagorean fuzzy environment, including Pythagorean Dombi fuzzy weighted arithmetic averaging (PDFWAA) operator, Pythagorean Dombi fuzzy weighted geometric averaging (PDFWGA) operator, Pythagorean Dombi fuzzy ordered weighted arithmetic averaging operator and Pythagorean Dombi fuzzy ordered weighted geometric averaging operator. Further, this paper presents several advantageous characteristics, including idempotency, monotonicity, boundedness, reducibility and commutativity of preceding operators. By utilizing PDFWAA and PDFWGA operators, this article describes a multicriteria decision-making (MCDM) technique for solving MCDM problems. Finally, a numerical example related to selection of a leading textile industry is presented to illustrate the applicability of our proposed technique.     

Computational noise effects on adaptive filter algorithms

Finite word length arithmetic roundoff noise in adaptive filter algorithms results in statistical variations in the filter weight vector about the infinite precision arithmetic weight vector. These roundoff errors may be modeled as a statistically non stationary driving noise affecting weight mean and covariance convergence. Mean and covariance expressions and bounds are desired for word lengths in fixed-point arithmetic by making use of multiplication roundoff error models. The adaptive filter algorithms consist of the LMS algorithm, the Widrow-Hoff LMS algorithm, pilot-vector algorithm and clipped vector algorithm. All of these algorithms can be implemented on-line and real-time. However, only the behavior of the LMS algorithm is reported here. The implementation of the adaptive filter algorithms in finite word length arithmetic is most evident in minicomputer, microprocessor, and dedicated digital signal processors for on-line real-time signal identification and parameter estimation in many disciplines. Radar signal processing, adaptive beam forming, acoustic signal identification, communication channel enhancement have a definite need for advanced filtering concepts. Our adaptive algorithms are typically employed in these filter configurations. These filters can also be employed in phase distortion equalizers. A particular advantage of these filters is that they can be trained to equalize a variety of distortions. Should a particular distortion scenario change in time, the filters can be made to easily adapt to the new problem.     

基于改进谱平滑策略的IMCRA算法及其语音增强

噪声谱估计算法在单通道语音增强方法中起着重要作用,为了改善噪声谱估计算法对噪声的估计和更新能力,结合最小统计（MS）算法,对改进的基于控制的递归平均（IMCRA）噪声谱估计算法的递归平均参数进行改进,并用一阶递归的方式对平滑功率谱的最小值进行改进。采用谱减法对含噪语音信号作去噪处理,从客观和主观两方面对不同算法的性能进行评价,对比分析不同噪声不同信噪比下增强前后语音的分段信噪比（segSNR）、PESQ得分、MOS得分。实验结果表明,提出的方法能够更好地跟踪噪声信号变化,改善语音质量。