Constrained total least squares algorithm for passive location based on bearing-only measurements

The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.     

Outliers, inliers and the generalized least trimmed squares estimator in system identification

The least trimmed squares estimator (LTS) is a well known robust estinaator in terms of protecting the estimatefrom the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity O( N In N) for large N, where N is the number of measurements. We also showthat though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers.     

Outliers, inliers and the generalized least trinuned squares estimator in system identification

The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity O( N In N) for large N, where N is the number of measurements. We also show that though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers.     

Input–output data filtering based recursive least squares identification for CARARMA systems

This paper uses an estimated noise transfer function to filter the input–output data and presents filtering based recursive least squares algorithms (F-RLS) for controlled autoregressive autoregressive moving average (CARARMA) systems. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the recursive least squares method can be used to estimate the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed F-RLS algorithm has a high computational efficiency because the dimensions of its covariance matrices become small and can generate more accurate parameter estimation compared with other existing algorithms.     

Parameterized eigensolution technique for solving constrained least squares problems ∗

This paper aims to introduce an algorithm for solving large scale least squares problems subject to quadratic inequality constraints. The algorithm recasts the least squares problem in terms of a parameterized eigenproblem. A variant of k-step Arnoldi method is determined to be well suited for computing the parameterized eigenpair. A two-point interpolating scheme is developed for updating the parameter. A local convergence theory for this algorithm is presented. It is shown that this algorithm is superlinearly convergent.     

Boosting the partial least square algorithm for regression modelling

Boosting algorithms are a class of general methods used to improve the general performance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.     

Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in α-stable noise

Although the least mean pth power (LMP) and normalized LMP (NLMP) algorithms of adaptive Volterra filters outperform the conventional least mean square (LMS) algorithm in the presence of α-stable noise, they still exhibit slow convergence and high steady-state kernel error in nonlinear system identification. To overcome these limitations, an enhanced recursive least mean pth power algorithm with logarithmic transformation (RLogLMP) is proposed in this paper. The proposed algorithm is adjusted to minimize the new cost function with the p-norm logarithmic transformation of the error signal. The logarithmic transformation, which can diminish the significance of outliers under α-stable noise environment, increases the robustness of the proposed algorithm and reduces the steady-state kernel error. Moreover, the proposed method improves the convergence rate by the enhanced recursive scheme. Finally, simulation results demonstrate that the proposed algorithm is superior to the LMP, NLMP, normalized least mean absolute deviation (NLMAD), recursive least squares (RLS) and nonlinear iteratively reweighted least squares (NIRLS) algorithms in terms of convergence rate and steady-state kernel error.     

Suboptimal input signals for linear system identification†

It is well known that inputs for identification experiments can be synthesized on the basis of optimizing a certain performance criterion, such as variances of parameters, estimates of weighting functions and probabilities of transmission error. Such signals give the best possible performance for given constraints of input energy, observation time and nature of noise. The question arises whether one can choose signals on the basis of other possibly more intuitive or physical criteria and see what kind of performance they give w.r.t. optimal inputs and common signals, as unit step inputs. These signals, which fit between the two extremes are here referred to as suboptimal.

The measure of performance is here taken to be the sum of parameter variances. It is shown that suboptimal inputs on the basis of maximizing the output signal/noise ratio, matching the system bandwidth and of pseudo-random binary noise nature can be selected for a given system. It is, however, also indicated that this is not a general method, since for certain systems no suboptimal performance was reached.     

Nonlinear system identification—Application for industrial hydro-static drive-line

The goal of the paper is to describe the added value and complexities of nonlinear system identification applied to a large scale industrial test setup. The additional important insights provided by the frequency domain nonlinear approach are significant and for such systems the nonlinear system identification is important, for example to estimate the noise and non-linearities levels, which can indicate mechanical and configuration issues. It is not the goal to provide a final full-scale model, but to explore what is the applicability of the nonlinear system identification theories for a complex multi-physical non-academic test-case.     

The discrete collocation method for Fredholm–Hammerstein integral equations based on moving least squares method

The purpose of this paper is to investigate the discrete collocation method based on moving least squares (MLS) approximation for Fredholm–Hammerstein integral equations. The scheme utilizes the shape functions of the MLS approximation constructed on scattered points as a basis in the discrete collocation method. The proposed method is meshless, since it does not require any background mesh or domain elements. Error analysis of this method is also investigated. Some numerical examples are provided to illustrate the accuracy and computational efficiency of the method.     

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algo- rithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assign- ment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate as- sociation cost; hence, much of the procedure time is saved. In the 2-stage asso- ciation algorithm, a large number of false location points are eliminated from can- didate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.     

Recursive identification for multi-input–multi-output Hammerstein–Wiener system

In this paper, an identification method based on the recursive auxiliary variable least squares algorithm is proposed for a multi-input–multi-output Hammerstein–Wiener system with process noise. In the proposed identification method, the system is converted into the multivariate regression form under the condition that the nonlinear block in the output part is invertible. Then, the auxiliary variable is constructed, the parameters of the regression equations are identified, and the system parameter matrices can be obtained by matrix composition of the parameter product matrix. A theoretical analysis showed that the proposed method has uniform convergence when the process noise is white and has a finite variance. The effectiveness of the proposed method is validated through the experiments.     

Scannet—a spectroscopic multimethod system for computer-assisted structural identification

The SCANNET computer program system is a general and efficient tool for helping the chemist to solve chemical structures. The system represents an entirely new philosophy in the management and exploitation (computer-assisted structural identification) of spectral databases in terms of six different analytical methods, namely: nuclear magnetic resonance spectrometry (¹³C-NMR, ¹H-NMR), infrared spectrometry (IR), mass spectrometry (MS), Raman spectrometry (RA) and ultraviolet spectrometry (UV). The system gives the spectroscopist a useful tool to document the results of his work (to create his own collection of spectra), and also to identify compounds which are represented in his own or a commercial database.     

Fast 2-D 8×8 discrete cosine transform algorithm for image coding

A new fast two-dimension 8×8 discrete cosine transform (2D 8×8 DCT) algorithm based on the charac-teristics of the basic images of 2D DCT is presented. The new algorithm computes each DCT coefficient in turn more independently. Hence,the new algorithm is suitable for 2D DCT pruning algorithm of prun-ing away any number of high-frequency components of 2D DCT. The proposed pruning algorithm is more efficient than the existing pruning 2D DCT algorithms in terms of the number of arithmetic opera-tions,especially the number of multiplications required in the computation.     

ℓ1-Regression based subdivision schemes for noisy data

Fitting curve and surface by least-regression is quite common in many scientific fields. It, however cannot properly handle noisy data with impulsive noises and outliers. In this article, we study ${\ell }_1$-regression and its associated reweighted least squares for data restoration. Unlike most existing work, we propose the ${\ell }_1$-regression based subdivision schemes to handle this problem. In addition, we propose fast numerical optimization method: dynamic iterative reweighted least squares to solve this problem, which has closed form solution for each iteration. The most advantage of the proposed method is that it removes noises and outliers without any prior information about the input data. It also extends the least square regression based subdivision schemes from the fitting of a curve to the set of observations in 2-dimensional space to a $p$-dimensional hyperplane to a set of point observations in $(p+1)$-dimensional space. Wide-ranging experiments have been carried out to check the usability and practicality of this new framework.     

Block approach—tabu search algorithm for single machine total weighted tardiness problem

Problem of scheduling on a single machine to minimize total weighted tardiness of jobs can be described as follows: there are n jobs to be processed, each job has an integer processing time, a weight and a due date. The objective is to minimize the total weighted tardiness of jobs. The problem belongs to the class of NP-hard problems. Some new properties of the problem associated with the blocks have been presented and discussed. These properties allow us to propose a new fast local search procedure based on a tabu search approach with a specific neighborhood which employs blocks of jobs and a compound moves technique. A compound move consists in performing several moves simultaneously in a single iteration of algorithm and allows us to accelerate the convergence to good solutions. In the algorithm, we use an idea which decreases the complexity for the search of neighborhood from O(n³) to O(n²). Additionally, the neighborhood is reduced by using some elimination criteria. The method presented in this paper is deterministic one and has not any random element, as distinct from other effective but non-deterministic methods proposed for this problem, such as tabu search of Crauwels, H. A. J., Potts, C. N., & Van Wassenhove, L. N. (1998). Local search heuristics for the single machine total weighted tardiness Scheduling Problem. INFORMS Journal on Computing, 10(3), 341–350, iterated dynasearch of Congram, R. K., Potts C. N., & Van de Velde, S. L. (2002). An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problem. INFORMS Journal on Computing, 14(1), 52–67 and enhanced dynasearch of Grosso, A., Della Croce, F., & Tadei, R. (2004). An enhanced dynasearch neighborhood for single-machine total weighted tardiness scheduling problem. Operations Research Letters, 32, 68–72. Computational experiments on the benchmark instances from OR-Library (http://people.brunel.ac.uk/mastjjb/jeb/info.html) are presented and compared with the results yielded by the best algorithms discussed in the literature. These results show that the algorithm proposed allows us to obtain the best known results for the benchmarks in a short time. The presented properties and ideas can be applied in any local search procedures.