This paper deals with the problem of stability for systems with delay varying in an interval. A new Lyapunov functional, which makes use of the information of both the lower and upper bounds of the interval time-varying delay, is proposed to derive some new stability criteria. Furthermore, the relationship of the time-varying delay and its lower bound and upper bound is taken into account. As a result, some less conservative delay-dependent stability criteria are obtained without ignoring any useful information in the derivative of Lyapunov functional, which are established in the forms of linear matrix inequalities. Numerical examples are provided to show that the obtained results are better than existing ones.
The problem of robustifying linear quadratic regulators (LQRs) for a class of uncertain affine nonlinear systems is considered. First, the exact linearization technique is used to transform an uncertain nonlinear system into a linear one and an optimal LQR is designed for the corresponding nominal system. Then, based on the integral sliding mode, a design approach to robustifying the optimal regulator is studied. As a result, the system exhibits global robustness to uncertainties and the ideal sliding mode dynamics is the same as that of the optimal LQR for the nominal system. A global robust optimal sliding mode control (GROSMC) is realized. Finally, a numerical simulation is demonstrated to show the effectiveness and superiority of the proposed algorithm compared with the conventional optimal LQR.
A class of latent ancestral graph for modelling the dependence structure of structural vector autoregressive(VAR) model affected by latent variables is proposed.The graphs are mixed graphs with possibly two kind of edges,namely directed and bidirected edges.The vertex set denotes random variables at dif- ferent times.In Gaussian case,the latent ancestral graph leads to a simple parameterization model.A modified iterative conditional fitting algorithm is presented to obtain maximum likelihood esti- mation of the parameters.Furthermore,a log-likelihood criterion is used to select the most appropriate models.Simulations are performed using illustrative examples and results are provided to demonstrate the validity of the methods.
A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.
Data envelopment analysis (DEA) is a mathematical programming approach to appraise the relative efficiencies of peer decision-making unit (DMU), which is widely used in ranking DMUs. However, almost all DEA-related ranking approaches are based on the self-evaluation efficiencies. In other words, each DMU chooses the weights it prefers to most, so the resulted efficiencies are not suitable to be used as ranking criteria. Therefore this paper proposes a new approach to determine a bundle of common weights in DEA efficiency evaluation model by introducing a multi-objective integer programming. The paper also gives the solving process of this multi-objective integer programming, and the solution is proven a Pareto efficient solution. The solving process ensures that the obtained common weight bundle is acceptable by a great number of DMUs. Finally a numeral example is given to demonstrate the approach.
The frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is a light-weight, cost-effective, high-resolution imaging radar, which is suitable for a small flight platform. The signal model is derived for FMCW SAR used in unmanned aerial vehicles (UAV) reconnaissance and remote sensing. An appropriate algorithm is proposed. The algorithm performs the range cell migration correction (RCMC) for continuous non-chirped raw data using the energy invariance of the scaling of a signal in the scale domain. The azimuth processing is based on step transform without geometric resampling operation. The complete derivation of the algorithm is presented. The algorithm performance is shown by simulation results.
An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing (OFDM) underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information. This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result, which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application; based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio, a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed, which realizes underwater communication with the low bit error rate through higher energy efficiency. The validity of the research content is verified through simulation and field experiments.
A method for maneuvering target tracking based on in- ductive factor of posture information is proposed.A distinguished characteristic of video frequency tracking is that it can capture the target posture changes from its picture easily,and the posture change means the motive model of the target will change.This information is very important to predict the trace of maneuvering target.Based on this idea,the quantified values of the target pos- ture change are obtained using Hough algorithm,this key values are defined as inductive factor of posture information,and then,the multiple grey trace predict models are established and the degrees of fuzzy subordinate values for every model are calculated with the inductive factor,the maneuvering extent values are determined by a new analysis method of stochastic differential equations for each model used to modify the degree of fuzzy subordinate values, these constitute the weighted values for every grey predict collec- tion.Finally,the synthesis predicting weighted result is obtained. The experimental results show that the new method is superior to the conventional algorithm.
To solve the problem of the precise strike for seeker-less missile, a cooperative guidance method of seeker-less missile and the beacon aircraft is proposed. Firstly, the guidance law considering the miss distance and line-of-sight (LOS) angle constraint is designed to achieve the precise strike on the target and satisfy the LOS angle constraint. On this basis, the tangential load of the beacon aircraft is designed to ensure that the remaining flight time (time-to-go) for the missile and the beacon aircraft converge to the same value within a finite time, thus the seeker-less missile can indirectly strike the target precisely. Simulation results validate the effectiveness of the proposed method in addressing the problem of cooperative strike on target, and compared with the cooperative guidance method in reference, the proposed cooperative guidance method is better in strike accuracy and demand overload.
A new method to design a quantum controller which directly controls the probability density function (PDF) of quantum systems is proposed. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed with uniform and non-uniform fields, respectively. Then a detailed control algorithm with convergence analysis is given for the small error case. By appropriately estimating the selected Lyapunov function, more accurate control effect is achieved. The proposed scheme provides a constructive method to find appropriate parameters for controller design.
This paper proposes a simple constant-stress accelerated life test (ALT) model from Burr type XII distribution when the data are Type-I progressively hybrid censored. The maximum likelihood estimation (MLE) of the parameters is obtained through the numerical method for solving the likelihood equations. Approximate confidence interval (CI), based on normal approximation to the asymptotic distribution of MLE and percentile bootstrap CI is derived. Finally, a numerical example is introduced and then a Monte Carlo simulation study is carried out to illustrate the proposed method.
Space-time adaptive processing (STAP) has been proven to be one of the best techniques capable of detecting weak moving targets in strong clutter environment and has been widely applied in airborne ground moving target indication (GMTI) radar. This paper applies an amplitude and phase estimation (APES) approach to two aspects of the STAP algorithm. Firstly, APES is applied to accurately describe the clutter characteristic in angle-Doppler domain. Then, APES is incorporated into the standard STAP algorithm to improve its performance without increasing transmitting/receiving channel and pulse number. The experimental examples show that the detection performance can be improved by using the APES technique, as well as the high computational complexity can be avoided.
To validate the potential space-time adaptive processing (STAP) algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, spacetime clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement (MCARM) experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.
An integration-centric approach is proposed to handle inadequate information in the system readiness level (SRL) assessment using the evidential reasoning (ER) algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system’s structure. The importance of the system’s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.
To tackle the challenges of intractable parameter tuning, significant computational expenditure and imprecise model-driven sparse-based direction of arrival (DOA) estimation with array error (AE), this paper proposes a deep unfolded amplitude-phase error self-calibration network. Firstly, a sparse-based DOA model with an array convex error restriction is established, which gets resolved via an alternating iterative minimization (AIM) algorithm. The algorithm is then unrolled to a deep network known as AE-AIM Network (AE-AIM-Net), where all parameters are optimized through multi-task learning using the constructed complete dataset. The results of the simulation and theoretical analysis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery methods. Furthermore, it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
This paper presents a joint high order statistics (HOS) and signal-to-noise ratio (SNR) algorithm for the recognition of multiple-input multiple-output (MIMO) radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.
An image segmentation algorithm of the restrained fuzzy Kohonen clustering network (RFKCN) based on highdimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional (3-D) feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image segmentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3-D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical analyses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.
Adopting“simultaneous transmitting, simultaneous receiving”operational scheme, instantaneous polarization radar (IPR) can measure target polarization scattering matrix (PSM) using only once target echoes in two orthogonal polarization channels. Firstly, signal model and signal process are advanced under narrowband condition. Secondly, measurement performances of two typical IPR waveforms are analyzed in detail. At last, field experiments are carried out using X-band IPR system designed by National University of Defense Technology (NUDT), China. Compared with results obtained by alternative polarization measurement scheme, following results can be obtained: the difference of relative amplitude measurement results is smaller than 2 dB and that of relative phase measurement results is smaller than 10?, verifying the validity of instantaneous polarization measurement scheme.
Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.
To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally tolerant to disturbances and sensitive to fault, the robustness and stability properties of the fault diagnosis scheme are established rigorously. Using the residual vector, a fault tolerant controller is established in order to guarantee the stability of the closed-loop system, and the controller law can be obtained by solving a set of linear matrix inequalities. Then, some relevant sufficient conditions for the existence of a solution are given by applying Lyapunov stability theory. Finally, a simulation example is performed to show the effectiveness of the proposed approach.
The principle and method of the narrow-band tomographic radar imaging (NBTRI) of the precession cone target are studied. Firstly, the motion model and electromagnetic scattering characteristics of the precession cone target are introduced. Secondly, based on the traditional NBTRI algorithm, a novel narrowband tomography clean radar imaging (NBTCRI) algorithm is proposed to enhance the image quality of NBTRI results. In addition, the resolution performance of the NBTRI algorithm is analyzed. Finally, based on the ideal scattering point model, this paper discusses the relationship between the precession angle and the estimated target size from NBTRI results. By using the target’s chamber data, NBTRI and NBTCRI results of the precession cone target are further analyzed, which indicates the effectiveness of the proposed method.
For a large-scale adaptive array, the heavy computational load and the high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. An efficient parallel digital beamforming (DBF) algorithm based on the least mean square algorithm (PLMS) is proposed. An appropriate method is found to partition the least mean square (LMS) algorithm into a number of operational modules, which can be easily executed in a distributed-parallel-processing fashion. As a result, the proposed PLMS algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. PLMS requires less computational load than that of the conventional parallel algorithms based on the recursive least square (RLS) algorithm, as well as it is easier to be implemented to do real time adaptive array processing. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. Finally, a scheme of the PLMS algorithm using distributed-parallel-processing system is also proposed. The simulation results demonstrate that the PLMS algorithm has the same interference cancellation performance as that of the conventional LMS algorithm. Moreover, the PLMS algorithm can obtain the same good beamforming performance, regardless how the algorithm is partitioned. It is expected that the proposed algorithm will be used in a large-scale adaptive array system to deal with real time adaptive digital beamforming processing.
The active contour model based on local image fitting (LIF) energy is an effective method to deal with intensity inhomogeneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better performance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the boundaries, and a global minimization of the active contour model is presented. In addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomogeneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.
This paper considers the problem of adaptive control for a class of multiple input multiple output (MIMO) nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor expand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adaptive neural network (NN) controller is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobally uniformly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.
To investigate the low-complex and high-precise tracking method, a novel single link tracking scheme based on UWB localization is proposed. Two antenna arrays are settled at the fixed station (FS) to receive the UWB signal from mobile terminal (MT), one FS is enough for the proposed scheme to track the MT. The UWB multipath detection algorithm for time difference of arrival (TDOA) estimation is presented and TDOA is thus adopted to estimate angle of arrival (AOA), avoiding the synchronization and complicated beamforming for AOA. The impacts of localization errors, concluding multipath and non-line-of-sight (NLOS) errors are simulated for the proposed track scheme. It is demonstrated that the simulation curve can match the real target moving, and the feasibility of the proposed scheme is proved.
Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable largescale long-running computing (RLLC) is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory (LANL) cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2 %, and it is a greatly improvement with 4 % compared with some typical methods. Finally, the managerial implications of the models are discussed.
The exact radar cross-section (RCS) measurement is difficult when the scattering of targets is low. Full polarimetric calibration is one technique that offers the potential for improving the accuracy of RCS measurements. There are numerous polarimetric calibration algorithms. Some complex expressions in these algorithms cannot be easily used in an engineering practice. A radar polarimetric coefficients matrix (RPCM) with a simpler expression is presented for the monostatic radar polarization scattering matrix (PSM) measurement. Using a rhombic dihedral corner reflector and a metallic sphere, the RPCM can be obtained by solving a set of equations, which can be used to find the true PSM for any target. An example for the PSM of a metallic dish shows that the proposed method obviously improves the accuracy of crosspolarized RCS measurements.
A fundamental premise of an accelerated testing is that the failure mechanism under elevated and normal stress levels should remain the same. Thus, verification of the consistency of failure mechanisms is essential during an accelerated testing. A new consistency analysis method based on the gray theory is proposed for complex products. First of all, existing consistency analysis methods are reviewed with a focus on the comparison of the differences among them. Then, the proposed consistency analysis method is introduced. Two effective gray prediction models, gray dynamic model and new information and equal dimensional (NIED) model, are adapted in the proposed method. The process to determine the dimension of NIED model is also discussed, and a decision rule is expanded. Based on that, the procedure of applying the new consistent analysis method is developed. Finally, a case study of the consistency analysis of a reliability enhancement testing is conducted to demonstrate and validate the proposed method.
The tracking, telemetry and command (TT&C) mission is extremely reliable for its characters of small time horizon and high redundancy. The combined forcing and failure biasing (CFFB) method that is usually used for simulating the unreliability of the highly dependable mission system seems not so efficient for the TT&C mission. The concept about the importance of failure transition is proposed based on the logical relationship between TT&C mission and its involved resources. Then, the importance is used for readjusting the transition rate of the failure transition when using the forcing and failure biasing during the simulation. Examples show that the improved CFFB method can evidently increase the occurrence of the TT&C mission failure event and decrease the sample variance. More redundancy of the TT&C mission leads to the improved CFFB method more efficient.
The problem of scheduling radar dwells in multifunction phased array radar systems is addressed. A novel dwell scheduling algorithm is proposed. The whole scheduling process is based on an online pulse interleaving technique. It takes the system timing and energy constraints into account. In order to adapt the dynamic task load, the algorithm considers both the priorities and deadlines of tasks. The simulation results demonstrate that compared with the conventional adaptive dwell scheduling algorithm, the proposed one can improve the task drop rate and system resource utility effectively.
A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed. The pre-registration is achieved by transforming the multi-pose models to the standard frontal model’s reference frame using the principal axis analysis algorithm. Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics. These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration. Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.
Traditional orthogonal strapdown inertial navigation system (SINS) cannot achieve satisfactory self-alignment accuracy in the stationary base: taking more than 5 minutes and all the inertial sensors biases cannot get full observability except the up-axis accelerometer. However, the full skewed redundant SINS (RSINS) can not only enhance the reliability of the system, but also improve the accuracy of the system, such as the initial alignment. Firstly, the observability of the system state includes attitude errors and all the inertial sensors biases are analyzed with the global perspective method: any three gyroscopes and three accelerometers can be assembled into an independent subordinate SINS (sub-SINS); the system state can be uniquely confirmed by the coupling connections of all the sub-SINSs; the attitude errors and random constant biases of all the inertial sensors are observable. However, the random noises of the inertial sensors are not taken into account in the above analyzing process. Secondly, the full-observable Kalman filter which can be applied to the actual RSINS containing random noises is established; the system state includes the position, velocity, attitude errors of all the sub-SINSs and the random constant biases of the redundant inertial sensors. At last, the initial selfalignment process of a typical four-redundancy full skewed RSINS is simulated: the horizontal attitudes (pitch, roll) errors and yaw error can be exactly evaluated within 80 s and 100 s respectively, while the random constant biases of gyroscopes and accelerometers can be precisely evaluated within 120 s. For the full skewed RSINS, the self-alignment accuracy is greatly improved, meanwhile the self-alignment time is widely shortened.
The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also, as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop. Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.
The acceleration of a high maneuvering target in signal processing is helpful to enhance the performance of the tracker and facilitate the classification of targets. At present, most of the research on acceleration estimation is carried out in cases of a single target with time-frequency analysis methods such as fractional Fourier transform (FRFT), Hough-ambiguity transform (HAT), and Wigner-Ville distribution (WVD), which need to satisfy enough time duration and sampling theorem. Only one reference proposed a method of acceleration estimation for multiple targets based on modified polynomial phase transform (MPPT) in the linear frequency modulation (LFM) continuous-wave (CW) radar. The method of acceleration estimation for multiple targets in the pulse Doppler (PD) radar has not been reported so far. Compressive sensing (CS) has the advantage of sampling at a low rate and short duration without sacrificing estimation performance. Therefore, this paper proposes a new method of acceleration estimation for multiple maneuvering targets with the unknown number based on CS with pulse Doppler signals. Simulation results validate the effectiveness of the proposed method under several conditions with different duration, measurement numbers, signal to noise ratios (SNR), and regularization parameters, respectively. Simulation results also show that the performance of the proposed method is superior to that of FRFT and HAT in the condition of multiple targets.
By analyzing the traditional star-tracking correction technology, this paper studies the star-tracking direct correction method which is based on star sensor dynamic separation initial displacement angles and main errors of inertial measure unit (IMU) in order to carry out automatic navigation and improve the hitting accuracy and quick reaction capability of near-earth flight vehicles. Through comprehensive application and improvement of the best correction factor method, this paper proposes the startracking comprehensive correction method which combines the two methods above-mentioned and the accelerometer dynamic error separation technology. The simulation results show that obvious effects can be achieved by using the star-tracking integrated correction method.
A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the otal nconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making (MCGDM) and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.
