KUHN-TUCKER NECESSARY CONDITIONS FOR （h,φ）-MULTIOBJECTIVE OPTIMIZATION PROBLEMS

Five kinds of cones are introduced, which are used to establish the constraints qualifications, under which the generalized Kuhn-Tucker necessary conditions are developed for a class of generalized (h,φ)-differentiable single-objective and multiobjective programming problems by using Motzkin‘s alternative theorem and Ben-Tal generalized algebraic operations.     

On the relative generalized hamming weights of a 4-dimensional linear code and a subcode with dimension one

Finite projective geometry method is effectively used to study the relative generalized Hamming weights of 4-dimensional linear codes,which are divided into 9 classes in order to get much more information about the relative generalized Hamming weights,and part of the relative generalized Hamming weights of a 4-dimensional linear code with a 1-dimensional subcode are determined.     

MODELLING RANDOM CONVEX SETS

A model is developed for convex-set valued data,which are the Minkowski sum of aconvex parameter set and a convex noise set.This model is generalized to include location and scaleparameters.A further generalization results in a model for convex sets which is analogous to aone-way analysis of variance models.In each case,estimators of the parameters are given which,under certain conditions,are shown to be consistent.Data generated from the model and theestimators are studied.An outlier detection method is examined.     

PARAMETER ESTIMATION OF EXPONENTIAL DISTRIBUTION

Because of the importance of grouped data, many scholars have been devoted to the study of this kind of data. But, few documents have been concerned with the thresh-old parameter. In this paper, we assume that the threshold parameter is smaller than the first observing point. Then, on the basis of the two-parameter exponential distribution, the maximum likelihood estimations of both parameters are given, the sufficient and necessary conditions for their existence and uniqueness are argued, and the asymptotic properties of the estimations are also presented, according to which approximate confidence intervals of the parameters are derived. At the same time, the estimation of the parameters is generalized, and some methods are introduced to get explicit expressions of these generalized estimations. Also, a special case where the first failure time of the units is observed is considered.     

Generalized intuitionistic fuzzy hybrid Choquet averaging operators

In this study a new hybrid aggregation operator named as the generalized intuitionistic fuzzy hybrid Choquet averaging(GIFHCA) operator is defined.Meantime,some desirable properties are studied, and several important cases are examined.Furthermore,we define the generalized Shapley GIFHCA (GS-GIFHCA) operator,which does not only overall consider the importance of elements and their ordered positions,but also globally reflect the correlations among them and their ordered positions.In order to simplify the complexity of solving a fuzzy measure,we further define the generalizedλ-Shapley GIFHCA(GλS-GIFHCA) operator.     

EXISTENCE AND ALGORITHM OF SOLUTIONS FOR GENERALIZED STRONGLY MIXED IMPLICIT QUASI-VARIATIONAL INEQUALITIES

The purpose of this paper is to introduce and study a new class of generalized strongly mixed implicit quasi-variational inequalities in Hilbert spaces, which includes the known class of generalized mixed implicit quasi-variational inequalities as a special case. By applying the auxiliary variational principle technique, the existence of solutions for this class of quasi-variational inequalities is proved. Moreover, a new iterative algorithm for computing approximate solutions is constructed and the convergence criteria for this iterative algorithm are also established.     

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.     

Construction of Optimal Mixed-Level Uniform Designs

The theory of uniform design has received increasing interest because of its wide application in the field of computer experiments. The generalized discrete discrepancy is proposed to evaluate the uniformity of the mixed-level factorial design. In this paper, the authors give a lower bound of the generalized discrete discrepancy and provide some construction methods of optimal mixed-level uniform designs which can achieve this lower bound. These methods are all deterministic construction methods...     

APPROXIMATE SUBDIFFERENTIALS AND NONSMOOTH ANALYSIS FINITE DIMENSIONS

Ioffe's approximate subdifferentials are reviewed and some of his resultsare generalized.An extension of the calculus of the approximate subdifferentials forthe sums to any finite number of functions is provided along with a generalizationof the Dubovitzkii-Milyutin theorem.The presentation also indicates some of thelimitations of nonsmooth analysis and optimization.Restriction to the class offunction which is suitable for most of the purposes in nonsmooth optimization issuggested.     

Linear generalized synchronization of chaotic systems with uncertain parameters

A more general form of projective synchronization, so called linear generalized synchronization （LGS） is proposed, which includes the generalized projective synchronization （GPS） and the hybrid projective synchronization （HPS） as its special cases, Based on the adaptive technique and Lyapunov stability theory, a general method for achieving the LGS between two chaotic or hyperehaotic systems with uncertain parameters in any scaling matrix is presented. Some numerical simulations are provided to show the effectiveness and feasibility of the proposed synchronization method.     

Best weight pattern evaluation based security constrained power dispatch algorithm

This paper presents a methodology which determines the allocation of power demand among the committed generating units while minimizes number of objectives as well as meets physical and technological system constraints. The procedure considers two decoupled problems based upon the dependency of their goals on either active power or reactive power generation. Both the problems have been solved sequentially to achieve optimal allocation of active and reactive power generation while minimizes operating cost, gaseous pollutants emission objectives and active power transmission loss with consideration of system operating constraints along with generators prohibited operating zones and transmission line flow limits. The active and reactive power line flows are obtained with the help of generalized generation shift distribution factors （GGDF） and generalized Z-bus distribution factors （GZBDF）, respectively. First problem is solved in multi-objective framework in which the best weights assigned to objectives are determined while employing weighting method and in second problem, active power loss of the system is minimized subject to system constraints. The validity of the proposed method is demonstrated on 30-bus IEEE power system.     

Weighted average consensus problem in networks of agents with diverse time-delays

This paper studies the weighted average consensus problem for networks of agents with fixed directed asymmetric unbalance information exchange topology. We suppose that the classical distributed consensus protocol is destroyed by diverse time-delays which include communication time-delay and self time-delay. Based on the generalized Nyquist stability criterion and the Gerschgorin disk theorem, some sufficient conditions for the consensus of multi-agent systems are obtained. And we give the expression of the weighted average consensus value for our consensus protocol. Finally, numerical examples are presented to illustrate the theoretical results.     

Cooperative games on convex geometries with a coalition structure

This paper is mainly to discuss cooperative games on convex geometries with a coalition structure,which can be seen as an extension of cooperative games with a coalition structure.For this kind of games,the cooperation among unions and within each union will be the convex sets,i.e., the feasible subsets of the coalition structure and that of each union belong to a convex geometry, respectively.The explicit form of the generalized Owen value for this kind of games is given,which can be seen as an extension of the Owen value.Furthermore,two special cases of this kind of games are researched.The corresponding payoff indices are also studied.Finally,an illustrative example is given.     

Spectral analysis and stabilization of a coupled wave-ODE system

In this paper, an interconnected wave-ODE system with K-V damping in the wave equation and unknown parameters in the ODE is considered. It is found that the spectrum of the system operator is composed of two parts： Point spectrum and continuous spectrum. The continuous spectrum consists of an isolated point 1 1/d, and there are two branches of the asymptotic eigenvalues： The first branch is accumulating towards 1 -2, and the other branch tends to -∞. It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space. As a consequence, the spectrum-determined growth condition and exponential stability of the system are concluded.     

Inference for repeated measures models under heteroscedasticity

This paper considers a widely used mixed effects model in repeated measures under heteroscedasticity. Hypotheses of the equality of the fixed effects and the simultaneous confidence intervals for all pair-wise differences are discussed.A generalized F-test has been proposed to test the equality of the fixed effects in the model,but simulation results for evaluating its performance have not been shown in the literature.Moreover,the generalized F-test cannot be used to deduce the simultaneous confidence intervals for all pair-wise differences of the fixed effects.The authors propose two new p-values to test the hypotheses of equality of the fixed effects and simultaneous confidence intervals of the differences of the effects based on the generalized pivotal quantities derived in this paper.The authors also compare the empirical performances of the proposed tests and the generalized F-test. The typeⅠerror rates and powers of these tests are evaluated using the Monte Carlo simulation.The simulation studies show that the generalized F-test does not perform well in terms of typeⅠerror rate under various sample size and parameter combinations.However,the typeⅠerror probabilities of the proposed tests are always close to the nominal value.It can also be seen that the simultaneous confidence intervals perform well.     

THE SECOND-ORDER CONDITIONS OF NONDOMINATED SOLUTIONS FOR C~(1,?) GENERALIZED MULTIOBJECTIVE MATHEMATICAL PROGRAMMING

In this paper,the generalized Hessian matrix and the generalized second-order directional(?)erivative for C~(1,1)vector functions are defined.The extension of the vector second-order Taylorexpansion is derived.The second-order necessary and sufficient conditions for the local nondominatedsolutions associated with the given convex cone and polyhedral convex cone of the generalizedmultiobjective mathematical programming problem with C~(1,1)constrained functions are discussed.     

Characterizations of semi-prequasi-invexity

Because of its importance in optimization theory,the concept of convexity has been generalized in various ways.With these generalizations,to seek some practical criteria for them is especially important.In this paper,some criteria are developed for semi-prequasi-invexity,which includes prequasi-invexity as the special case.Mutual characterizations among semi-prequasi-invex functions,strictly semi-prequasi-invex functions,and strongly semi-prequasi-invex functions are presented.     

Asymptotic Normality of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Fixed Design*

In generalized linear models with fixed design, under the assumption λ↑_n→∞ and other regularity conditions, the asymptotic normality of maximum quasi-likelihood estimator ^↑βn, which is the root of the quasi-likelihood equation with natural link function ∑i=1^n Xi（yi -μ（Xi′β）） = 0, is obtained, where λ↑_n denotes the minimum eigenvalue of ∑i=1^nXiXi′, Xi are bounded p × q regressors, and yi are q × 1 responses.     

Blind CFO estimation algorithm for OFDM systems by using generalized precoding and trilinear model

This paper discusses the blind carrier frequency offset (CFO) estimation for orthogonal frequency division multiplexing (OFDM) systems by utilizing trilinear decomposition and generalized precoding. Firstly, the generalized precoding is employed to obtain multiple covariance matrices which are requisite for the trilinear model, and then a novel CFO estimation algorithm is proposed for the OFDM system. Compared with both the joint diagonalizer and estimation of signal parameters via rotational invariant technique (ESPRIT), the proposed algorithm enjoys a better CFO estimation performance. Furthermore, the proposed algorithm can work well without virtual carriers. Simulation results illustrate the performance of this algorithm.