A Multilayer Recurrent Fuzzy Neural Network for Accurate Dynamic System Modeling

A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.

Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

Notes on the Scaling Function

Two properties are given in this paper about the scaling function: suppose Vj; j ∈ Z is a multiresolution analysis with a continuous scaling function φ which have compact support set and that φ the Fourier transform of φ is a continuous real function, compactly supported, then φ(0) ≠ 0 and when supp φ = [a1,b1]∪[a2,b2](b1 < a2,0 < a2), then we havea1 ≤ 0, 0 < b1, a1 < b2/2 ≤ b1, 2π < b2 - a1 ≤ 8π.

Unknown parameter’s variance-covariance propagation and calculation in generalized nonlinear least squares problem

The unknown parameter’s variance-covariance propagation and calculation in the generalized nonlinear least squares remain to be studied now, which didn’t appear in the internal and external referencing documents. The unknown parameter’s vari- ance-covariance propagation formula, considering the two-power terms, was concluded used to evaluate the accuracy of unknown parameter estimators in the generalized nonlinear least squares problem. It is a new variance-covariance formula and opens up a new way to evaluate the accuracy when processing data which have the multi-source, multi-dimensional, multi-type, multi-time-state, different accuracy and nonlinearity.

2-D elastic wave modeling with frequency-space 25-point finite-difference operators

Numerical simulation in the frequency-space domain has inherent advantages, such as： it is possible to simulate wave propagation from multiple sources simultaneously; there are no cumulative errors; only the interesting frequencies can be selected; and it is more suitable for wave propagation in viscoelastic media. The only obstacle to using the method is the requirement of huge computer storage. We extend the compressed format for storing the coefficient matrix. It can reduce the required computer storage dramatically. We get the optimal coefficients by least-squares method to suppress the numerical dispersion and adopt the perfectly matched layer （PML） boundary conditions to eliminate the artificial boundary reflections. Using larger grid intervals decreases computer storage requirements and provides high computational efficiency. Numerical experiments demonstrate that these means are economic and effective, providing a good basis for elastic wave imaging and inversion.

New Method for the Determination of Ionization Constants of Polyprotic Weak Acid

A new method for the determination of ionization constants of polyprotic weak acids is presented. Based on dissociation equilibrium,mass balance and charge balance, the mathematic model is established and the non linear least squares Gauss Newton method is applied to numerically solve the model equations. In order to get the concentration of hydrogen ion, the Debye Hückel equation is used to calculate its activity coefficient. The ionization constants of H 2SO 3 and H 2C 2O 4 obtained by this method are in good agreement with the literature values.

三角拖运策略下的拖运企业联盟利润分配

为帮助拖运企业实现高运转与低成本的均衡化运营,提出一种基于“三角拖运”策略的拖运企业联盟模式。首先,构建一个两阶段利润分配模型,用于完成联盟内集卡调度、集装箱路径规划及联盟内部企业间的利润分配;然后,以一个集装箱港口的拖运企业联盟为例,利用CPLEX完成对模型验证与分析。算例结果表明:基于“三角拖运”的联盟模式可有效提高拖运效率和联盟利润;基于利润与贡献的最小二乘值利润分配方法中,自身利润越大、贡献越高的企业分得的联盟利润越多,但随着利润重要度参数的增加,贡献较高的企业分得的联盟利润逐渐下降;客户满意度、资源投入、风险承担能力等影响因素的权重与企业的分配利润呈正相关。与平均分配法及Shapley值法等传统利润分配方法对比,本文提出的两阶段利润分配模型有效提高了联盟企业合作的稳定性。

The Influence of Spiritual Values on Work Motivation, Management Control, and Quality of Financial Information on IFIs in Indonesia

This study aims to investigate the influence of spiritual values on work motivation, management control, and the quality of financial information on Islamic financial institutions （IFIs） in Indonesia. Spiritual values are measured using four reflexive indicators, namely, Shiddiq, Fathanah, Amanah, and Tabligh. Work motivation is formed by four formative indicators, namely, compensation, recognition, work experience, and openness. Management control variable is formed by four formative indicators, namely, planning, implementing, evaluating, and monitoring, and finally, the quality of financial information is manifested in four reflexive variables, namely, relevant, comparable, understandable, and consistent. Data are collected through questionnaires with 125 respondents as samples from three types of IFIs, such as Islamic banking, Islamic pawnshop, and Islamic insurance in Indonesia. Methods of data analysis include the use of partial least squares （PLS） to analyze the influence of spiritual values on work motivation, management control, and quality of financial information. The results of this study showed that the manifestation of spiritual values in IFIs is able to influence the quality of financial information through the media as motivation and management control, but directly spiritual values are not able to influence the quality of financial information. While work motivation and management control significantly influence the quality of financial information.

The improved element-free Galerkin method for three-dimensional transient heat conduction problems

With the improved moving least-squares (IMLS) approximation, an orthogonal function system with a weight function is used as the basis function. The combination of the element-free Galerkin (EFG) method and the IMLS approximation leads to the development of the improved element-free Galerkin (IEFG) method. In this paper, the IEFG method is applied to study the partial differential equations that control the heat flow in three-dimensional space. With the IEFG technique, the Galerkin weak form is employed to develop the discretized system equations, and the penalty method is applied to impose the essential boundary conditions. The traditional difference method for two-point boundary value problems is selected for the time discretization. As the transient heat conduction equations and the boundary and initial conditions are time dependent, the scaling parameter, number of nodes and time step length are considered in a convergence study.

On minimum cavitation number of the ventilated supercavity in water tunnel

A numerical method consisted of the cavitation number correction and the model coefficient correction algorithms is presented to simulate the supercavity in water tunnel considering blockage and gravity effects based on the Logvinovich model.A model of the minimum cavitation number is also proposed based on the dimensional analysis theory,and the minimum cavitation number is formulated based on the model and numerical results using the nonlinear least square method(NLLS).The formula is verified by experiment to some extent.

Improved modified moving average analysis of T-wave alternans using least squares-based curve fitting method

T-wave alternans （TWA） in surface electrocardiograph （ECG） signal is considered a marker of abnormal ventricular function which may be associated with ventricular tachy- cardia. Several methods have been developed in recent years to evaluate the important feature. One such method is known as modified moving average （MMA） analysis, which performs well for different levels of TWA, but it is sensitive to the noise in T-waves. In this paper we propose an improved MMA algorithm, which adds a stage of T-wave curve fitting for the MMA method before intermediate averaging. The curve fitting is performed by means of least square method technique. Our assessment study demon- strates the improved performance.

Some results on the regularization of LSQR for large-scale discrete ill-posed problems

LSQR, a Lanczos bidiagonalization based Krylov subspace iterative method, and its mathematically equivalent conjugate gradient for least squares problems(CGLS) applied to normal equations system, are commonly used for large-scale discrete ill-posed problems. It is well known that LSQR and CGLS have regularizing effects, where the number of iterations plays the role of the regularization parameter. However, it has long been unknown whether the regularizing effects are good enough to find best possible regularized solutions. Here a best possible regularized solution means that it is at least as accurate as the best regularized solution obtained by the truncated singular value decomposition(TSVD) method. We establish bounds for the distance between the k-dimensional Krylov subspace and the k-dimensional dominant right singular space. They show that the Krylov subspace captures the dominant right singular space better for severely and moderately ill-posed problems than for mildly ill-posed problems. Our general conclusions are that LSQR has better regularizing effects for the first two kinds of problems than for the third kind, and a hybrid LSQR with additional regularization is generally needed for mildly ill-posed problems. Exploiting the established bounds, we derive an estimate for the accuracy of the rank k approximation generated by Lanczos bidiagonalization. Numerical experiments illustrate that the regularizing effects of LSQR are good enough to compute best possible regularized solutions for severely and moderately ill-posed problems, stronger than our theory, but they are not for mildly ill-posed problems and additional regularization is needed.

HASM-AD Algorithm Based on the Sequential Least Squares

The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.

Robust structured total least squares algorithm for passive location

A new approach called the robust structured total least squares(RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares(WSTLS)framework and improved based on the robust estimation theory.Moreover, the improved Danish weight function is proposed according to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algorithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.