Optimal Weight Vector Research Articles

Approaches for multicriteria decision-making based on the hesitant fuzzy best\u2013worst method

Hesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

Variable step-size normalized subband adaptive filtering algorithm for self-interference cancellation

Self-interference (SI) is usually generated by the simultaneous transmission and reception of the same system, which has a negative effect on the subsequent processing of the received signal. To solve this problem, a variable step-size normalized subband adaptive filtering (NSAF) algorithm is proposed to eliminate the SI. First, the optimal weight vector of each subband is obtained based on minimizing the mean-square deviation between the optimal weight vector and its estimation, and then the power of the residual SI is estimated by the shrinkage method. To eliminate the influence of input noise estimation bias on the algorithm, we estimate the input noise power of each subband and replace the fixed input noise power at each iteration. Then, the convergence, steady-state behavior and computational complexity are analyzed to prove the feasibility of the proposed algorithm. Simulation results demonstrate that the proposed algorithm is superior to cited algorithms for SI cancellation. Moreover, for the direction of arrival estimation, the received signal processed by the proposed algorithm is more accurate than those cited algorithms.

Cloud Trust-Driven Hierarchical Sharing Method of Internet of Things Information Resources

Short information sharing time is one of the problems to be solved in the traditional Internet. Therefore, this paper proposes a hierarchical simulation of the Internet of Things sharing structure framework that trusts the cloud to drive Internet information resource sharing. By setting thresholds and iterative adjustment parameters, a complementary judgment matrix is constructed to obtain the minimum nonnegative deviation value and the optimal weight vector. This paper sorts according to the value of the Internet information security model, obtains the optimal model to avoid human tampering, and designs the information resource acquisition process to ensure the reliability of the source data. We use radio frequency identification (RFID) equipment in the preprocessing of massive heterogeneous data in the Internet of Things. In a network environment where resources are limited and heterogeneous are fully considered, the trust-based adaptive detection algorithm is used to evaluate the credibility of the trust-driven algorithm for hierarchical information resource sharing services in the cloud environment of the Internet of Things. We propose a cloud trust-driven hierarchical information resource sharing Internet information resource model. Firstly, the key characteristics of hierarchical information resource sharing are analyzed. Then, a hierarchical information resource sharing model was established by using specific constraints, trust steepness function, cloud trust evaluation criteria, and trust constraint coefficient. Finally, an example of IoT system is designed to verify the effectiveness of the model. Experimental results show that, compared with the traditional model or algorithm, this model has a good hierarchical sharing effect of the underlying resource information.

SOFT: salient object detection based on feature combination using teaching-learning-based optimization

Salient object detection is a challenging task in the computer vision due to complexity of images and insufficiency of a single feature for saliency map generation. The performance of saliency detection depends upon the manner in which these visual features are combined. In this paper, we propose an efficient features combination model in which different features are combined in an appropriate manner. To meet this objective, a metaheuristic optimization algorithm called teacher-learning-based optimization (TLBO) is employed to find an optimal weight vector for feature combination. TLBO is a parameterless, efficient, and robust algorithm as it does not require parameter tuning during its implementation. Furthermore, the optimization criteria of TLBO is modified using a novel fitness function for improved performance. To check the performance of the proposed model, we have conducted extensive experiments over six benchmark datasets and compared the results against seven state-of-the-art methods.

A Study on Radar Target Detection using Space Time Adaptive Processing Algorithm and LCMV Algorithm

In this paper, we study the directionof arrival estimation of the desired target in adaptive array MV algorithm to update the weight, and the optimized weight removes the interference signal. The target signal is estimated using the optimized weight vector and the high resolution the direction of arrival estimation MUSIC algorithm. We calculate the inverse of the correlation matrix using the QR method to reduce the processing power consumption of the optimized weight. The optimal weight vector is applied to the proposed algorithm to estimate the desired target direction from the output power spectrum. The performance of the proposed method is compared with the existing method by simulation. The experimental method estimates three targets from the antenna received signal. The existing method did not estimate the three desired targets at [-30o,-20, -10o]. The proposed method accurately estimates the desired three targets at [-30o,-20, -10o]. In the [-10o, 0, 10o] target estimation, the existing method reduces the estimated resolution of the target, but the proposed method accurately estimates the target. We proved that the proposed method in the simulation was superior to the existing method.

Robust Constrained Generalized Correntropy and Maximum Versoria Criterion Adaptive Filters

The constrained least mean square algorithm is extensively used for adaptive filtering applications which need to satisfy a set of linear constraints. However, it is not robust when non-Gaussian or impulsive noise is present at the error sensor. To effectively overcome this issue, in this brief, we propose the constrained generalized maximum correntropy criterion (CGMCC) algorithm. To further improve steady state convergence behavior of the adaptive filter in such scenarios, we also propose the constrained maximum Versoria criterion (CMVC) algorithm. The expressions of the optimal weight vector for both the proposed algorithms are derived. Bound on learning rates are also derived to ensure the stability of the proposed adaptive systems in the mean square sense. The computational expense of the proposed algorithms is also studied. Simulation studies carried out demonstrate the improvement in steady state convergence performance and robustness achieved by the proposed algorithms.

A novel stochastic resonance model based on bistable stochastic pooling network and its application

• A novel stochastic resonance model based on bistable stochastic pooling network is proposed. • The least mean square algorithm is used to optimize the BSPN output vector optimized by random noise with linear weighting. • The SNR of the output signal is deduced, and the change of SNR with the number of network nodes is discussed. • Some examples are used to verify the weak signal detection capability of the BSPN model. Analysing the vibration and sound signals of machine components is the primary approach for machine condition monitoring and fault diagnosis. However, due to the special working operating conditions of rotating machinery, the collected signals often contain strong noise components generated by other parts of the machine and harsh environment. These noises severely affect the analysis and processing of the target signal. Stochastic resonance (SR) is an effective technique to extract and enhance periodic or aperiodic signals submerged in noise. Consequently, SR has been widely used for fault diagnosis of rotating machinery. In this study, a bistable stochastic pooling network (BSPN) model based on the traditional SR model is proposed to improve the efficiency of weak fault diagnosis. The least mean square algorithm is used to perform linear weighted optimization on the output vector of random noise-optimized BSPN. At the same time, the optimal weight vector of the random stochastic pooling networks with any number of nodes is obtained. Subsequently, analog signals are used to examine the output signal-to-noise ratio (SNR) of the BSPN. Finally, the efficacy of BSPN system is validated through bearing data collected by two different experimental systems. The experimental results indicate that ordinary array system cannot avoid frequency conversion interference, so it is unable to extract extremely weak fault signals. On the contrary, the BSPN system can accurately detect the weak.

Model Selection and Model Averaging for Nonlinear Regression Models

This paper develops a nonlinear model averaging method (NLMA) and constructs the corresponding weight choosing criterion which we call NLC criterion for models that are nonlinear with respect to their parameters. NLC can be applied as an information criterion to model selection for nonlinear regression models as well. NLC is based on the idea of Mallows Cp which can be regarded as an unbiased estimator of the mean squared error (MSE) and takes the nonlinearity of the models into consideration. Under some regularity conditions, we investigate into the properties of NLMA and NLC. We show that the weight vector chosen by minimizing NLC converges in probability to the optimal weight vector that minimizes the risk. It’s also proved that the NLMA estimator based on the weight vector selected by NLC minimizes the risk of estimation asymptotically. Furthermore, the distribution of NLMA estimator is derived. For finite sample property, simulation results show that NLMA performs much better than every single-model approximation and other model selection and model averagingmethods, achieving relatively smaller sample MSE.

Logical efficiency decomposition for general two-stage systems in view of cross efficiency

Most of the previous cross efficiency evaluation methods consider the evaluated system as a “black box” system. This probably leads to misleading results. For example, the system is efficient but all of its divisions are inefficient. This paper firstly investigates the cross efficiency for a general two-stage system. In such system, some outputs of one stage are taken as inputs of the other stage, and some other outputs directly become the final products. Since the optimal weight vector for the self-evaluation of a decision making unit (DMU) may be non-unique, we apply the leader-follower method for the decomposition of the system's efficiency and then adopt the optimal weights to obtain the peer-evaluation. Then, multiplicative hesitant fuzzy elements (MHFEs) are applied to denote the ratio of all possible cross efficiencies of any two DMUs. Based on MHFEs, multiplicative hesitant fuzzy preference relations (MHFPRs) are used to express these relationships comprehensively. Furthermore, the optimization models for deriving acceptably consistent MHFPRs with the minimum total adjustment and the minimum number of adjusted elements are built when the consistency of MHFPRs is unacceptable. An algorithm for decomposing the overall efficiency according to the cross efficiency and acceptably consistent MHFPRs in the setting of the general two-stage system is provided. Finally, our method is applied to the analysis of nine top universities in China.

Phase-Only Space-Time Adaptive Processing

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling; it derives the optimum phase-only STAP, namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the $N$ -torus of phase-only complex vectors, where $N$ is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phase-only STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.

A complementary webometric ranking system based on the website quality and traffic measures: A study focusing on top-ranked world universities

Ranking of universities regarding their web-based activities plays a pivotal role in promoting scientific advancement since it motivates the open access accessibility to scientific results. In this study, a new ranking system based on the website quality factors and traffic evaluation was proposed. Since top-ranked universities are usually considered as the standard models for lower ranked ones, the focus of this study was on top-ranked universities. The proposed ranking was compared with well-known Webometrics ranking system. The website traffic and quality assessment were acquired for websites of top-ranked world universities and the correlation between these indices and the Webometrics ranking was evaluated. The summation of the weighted value of obtained measures according to an optimal weight vector obtained by a genetic algorithm framework was used for ranking purposes. The results showed that the website total traffic size was correlated with Webometrics rank (R≈-0.6, p< 0.01). Also, using the weighted value of website quality and traffic measures, the proposed ranking system could predict Webometrics ranking by the accuracy of up to 69%. Even though the method was proposed for universities, it could be applied for ranking other types of centers or companies, provided that the suitable cost function for the genetics algorithm framework was defined.

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.

Robustness D3LS beamforming algorithm against the uncertainties in a uniform linear array radar of dipole antenna

SummaryIn practical terms, direct data domain least squares (D3LS) beamforming algorithm performance is severely decreased by the array steering vector uncertainty. In this paper, we investigate the performance of D3LS approach for beamforming. Uniform linear antenna array radar of dipole antenna is considered in the presence of mutual coupling between the elements of the array. The performance of D3LS beamforming algorithm is severely affected by uncertainty caused by mutual coupling. In order to test the robustness of the D3LS approach against uncertainty, a quadratic inequality constraint is considered to optimize weight vector of D3LS beamforming algorithm. The convex optimization method is used to calculate optimized weight vector. The proposed convex optimization problem is solved by barrier method. It is a subset of Interior‐point numerical analysis methods. Several different scenarios are used to evaluate the proposed algorithm in MATLAB.

Detection and Isolation of Wheelset Intermittent Over-Creeps for Electric Multiple Units Based on a Weighted Moving Average Technique

Wheelset intermittent over-creeps (WIOs), i.e., slips or slides, can decrease the overall traction and braking performance of Electric Multiple Units (EMUs). However, they are difficult to detect and isolate due to their small magnitude and short duration. This paper presents a new index called variable-to-minimum difference (VMD) and a novel technique called weighted moving average (WMA). Their combination, i.e., the WMA-VMD index, which uses correlation information to find an optimal weight vector (OWV) for the VMD indices within a time window, is employed to detect and isolate WIOs in real time. The uniqueness of the OWV is proven, and its properties such as the symmetrical structure are revealed. WIO detectability and isolability conditions of the WMA-VMD index are provided, leading to the property analyses of two nonlinear, discontinuous operators, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\min $ </tex-math></inline-formula> and VMD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>i</i></sub> . Experimental studies are conducted based on practical running data and a hardware-in-the-loop platform of an EMU, which show the effectiveness of the developed method.

Подавление узкополосных стационарных помех в телеметрическом комплексе на основе автокомпенсатора с удаленными каналами.

The problem of combating stationary interference in the areas of operation of ground-based telemetry systems is considered. One of the most annoying types of interference is high-power narrowband TV interference. They are emitted from television towers and their position is known in advance. A solution to this problem is proposed by using a multichannel auto-compensator with a non-standard arrangement of compensation channels. An analytical solution is obtained for the optimal weight vector of the auto-compensator of interference, based on the method of power vectors. This method does not require direct inversion of the interference correlation matrix. The computational complexity of the proposed method is estimated and it is shown that it has a much lower computational complexity compared to the method of direct inversion of the interference correlation matrix. The results of numerical simulation of the interference suppression coefficient are presented. Its effectiveness has been shown.

Two-Stage Beamforming for Rejecting Interferences Using Deep Neural Networks

Established beamforming techniques for finding the optimal weight vectors of an antenna array, such as minimum mean squared error (MMSE) and minimum variance distortion-less response (MVDR), often require the direction of arrival (DoA) of the desired signal. In this article, we introduce a neural beamformer to estimate the desired signal in the presence of noise and interferences, without requiring the DoA. The proposed neural beamformer consists of a convolutional neural network (CNN) to estimate the interference vector from signals received at a subarray of the antennas and a bidirectional long short-term memory (bi-LSTM) to estimate the samples of the desired signal. The output signal- to- interference- and noise ratio (SINR) with the proposed neural beamformer is 10dB higher than the conventional beamformers when the number of available snapshots of the received signal is as low as 100. The beamformer can estimate the desired signal when input interference-to-signal ratio (ISR) is as high as 35dB and input signal-to noise ratio (SNR) is as low as -10 dB.

Model averaging prediction for time series models with a diverging number of parameters

An important problem with the model averaging approach is the choice of weights. In this paper, a generalized Mallows model averaging (GMMA) criterion for choosing weights is developed in the context of an infinite order autoregressive (AR(∞)) process. The GMMA method adapts to the circumstances in which the dimensions of candidate models can be large and increase with the sample size. The GMMA method is shown to be asymptotically optimal in the sense of achieving the lowest out-of-sample mean squared prediction error (MSPE) for both the independent-realization and the same-realization predictions, which, as a byproduct, solves a conjecture put forward by Hansen (2008) that the well-known Mallows model averaging criterion from Hansen (2007) is asymptotically optimal for predicting the future of a time series. The rate of the GMMA-based weight estimator tending to the optimal weight vector minimizing the independent-realization MSPE is derived as well. Both simulation experiment and real data analysis illustrate the merits of the GMMA method in the prediction of an AR(∞) process.

Single-Point Array Response Control: An Efficient Beampattern Synthesis Approach Based on the Maximum Magnitude Response Principle

This article considers the beampattern synthesis problem, and a single-point array response control (SPARC) algorithm is presented to adjust the array response at an arbitrary direction. Based on the theoretical analysis, it is found that the adaptive array theory-based optimal weight vector is a linear combination of the signal steering vector and the interference steering vector multiplied by a complex factor, and that the complex factor determines the array response at the interference direction. Inspired by this observation, the SPARC algorithm is presented to achieve quiescent pattern control. In contrast to other array response control-based pattern synthesis techniques, the SPARC algorithm obtains a deterministic expression of the complex factor after maximizing the magnitude response at the main-beam direction, and the closed-form expression of the designed weight vector is devised. Because of different pattern constraints in focused and shaped beampatterns, two low-complexity pattern synthesis schemes are presented employing the SPARC algorithm in an iterative manner. Moreover, the parameter selection principle of the SPARC and its comparison with other pattern synthesis methods are discussed. Simulation results show the effectiveness of the SPARC algorithm and the efficiency of the beampattern synthesis schemes.

Detection of intermittent faults based on an optimally weighted moving average [formula omitted] control chart with stationary observations

The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling’s T2 statistic to form an OWMA T2 control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven. Finally, simulations on a numerical example and a continuous stirred tank reactor process are carried out to give a comprehensive comparison among OWMA-TCC and several existing static and dynamic MSPM methods. The results show a superior IFD performance of the developed methods.

Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for unmanned aerial vehicles (UAVs), which have been widely deployed to conduct various missions, e.g., precision agriculture, forest monitoring, and border patrol. However, the unique features of aerial UAVs, including high-altitude manipulation, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of the direction-of-Arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14-dB SINR gain compared with five benchmark beamforming algorithms, including linearly constrained minimum variance (LCMV), position-based beamforming, diagonal loading (DL), robust capon beamforming (RCB), and robust LCMV algorithm.