A simple smooth exact penalty function for smooth optimization problem

For smooth optimization problem with equality constraints,new continuously differentiate penalty function is derived.It is proved exact in the sense that local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function under some nondegeneracy assumption. It is simple in the sense that the penalty function only includes the objective function and constrained functions,and it doesn’t include their gradients.This is achieved by augmenting the dimension of the program by a variable that controls the weight of the penalty terms.     

A Combinatorial Condition and Boolean Functions with Optimal Algebraic Immunity

This paper first proposes an infinite class of 2k-variable Boolean functions with high nonlinearity and high algebraic degree. Then an infinite class of balanced Boolean functions are proposed by modifying the above Boolean functions. This class of balanced Boolean functions have optimal algebraic degree and high nonlinearity. Both classes have optimal algebraic immunity based on a general combinatorial conjecture.     

Analyzing the Dynamics of Nonlinear Multivariate Time Series Models

This paper analyzes the dynamics of nonlinear multivariate time series models that is represented by generalized impulse response functions and asymmetric functions. We illustrate the measures of shock persistences and asymmetric effects of shocks derived from the generalized impulse response functions and asymmetric function in bivariate smooth transition regression models. The empirical work investigates a bivariate smooth transition model of US GDP and the unemployment rate.     

Fractal Solitons,Arbitrary Function Solutions,Exact Periodic Wave and Breathers for a Nonlinear Partial Differential Equation by Using Bilinear Neural Network Method

This paper extends a method, called bilinear neural network method(BNNM), to solve exact solutions to nonlinear partial differential equation. New, test functions are constructed by using this method. These test functions are composed of specific activation functions of single-layer model,specific activation functions of "2-2" model and arbitrary functions of "2-2-3" model. By means of the BNNM, nineteen sets of exact analytical solutions and twenty-four arbitrary function solutions of the dimensionally reduced p-gB KP equation are obtained via symbolic computation with the help of Maple. The fractal solitons waves are obtained by choosing appropriate values and the self-similar characteristics of these waves are observed by reducing the observation range and amplifying the partial picture. By giving a specific activation function in the single layer neural network model, exact periodic waves and breathers are obtained. Via various three-dimensional plots, contour plots and density plots,the evolution characteristic of these waves are exhibited.     

Improved artificial bee colony algorithm with mutual learning

The recently invented artificial bee colony (ABC) algorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems.It performs well in most cases,however,there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of finding a neighboring food source.This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor.The performance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algorithm and some classical versions of improved ABC algorithms.The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.     

Study on the System Design of Multiple Expert Systems Integrated Decision Support System

There has been an increasing interest in integrating decision support systems (DSS) and expert systems (ES) to provide decision makers a more accessible, productive and domain-independent information and computing environment. This paper is aimed at designing a multiple expert systems integrated decision support system (MESIDSS) to enhance decision makers' ability in more complex cases. The basic framework, management system of multiple ESs, and functions of MESIDSS are presented. The applications of MESIDSS in large-scale decision making processes are discussed from the following aspects of problem decomposing, dynamic combination of multiple ESs, link of multiple bases and decision coordinating. Finally, a summary and some ideas for the future are presented.     

Optimal Control of Teaching Process

It is clearly illustrated in[1 ] ,the quantity analysis of the dynamic process of teachingeffects has been clearly illustrated in the first reference,according to which we areinformed of the mathematic model of the teaching process;the function of teachers dwingthe process;as well as the influere on the process of comprehending ofthe students causedby several different functions.But that confines in the specific relationship betweenteaching and learly as far as a single different class,a concre…     

Some Properties of a Measure of Information Discrepancy

1　IntroductionBased on a group of axioms,a measure of information discrepancy among multipleinformation sources has been introduced in [7,8,10 ]( itabbreviated by FDOD measure) ,it has been shown that there is a close relationship between this measure and Shannonentropy,and it has many algebraic,analytic and statistic properties,such as non-negative,identity,symmetry,continuity,invariant for average,monotonicity,homogeneity,convexity,symmetric recursiveness,and so on. It has some peculiarpr…     

ROLE OF α-PSEUDO-UNIVEX FUNCTIONS IN VECTOR VARIATIONAL-LIKE INEQUALITY PROBLEMS

In this paper, we introduce a new class of generalized convex function, namely, a-pseudounivex function, by combining the concepts of pseudo-univex and α-invex functions. Further, we establish some relationships between vector variational-like inequality problems and vector optimization problems under the assumptions of α-pseudo-univex functions. Results obtained in this paper present a refinement and improvement of previously known results.     

A situation calculus based approach to dynamic management of e-business services

In the era of e-businesses, the traditional business services are greatly challenged by the ever-increasing demands from customers with various backgrounds and personalities. Large numbers of new e-businesses are driven by the needs of customers. The existing dynamics inherent in the customer needs require the corresponding dynamic management of services. Attempting to respond to customers in a rapid and intelligent way, this paper proposes a situation calculus based approach for dynamically managing e-Business services in the ubiquitous environment. By employing the formalism of the situation calculus to enable intelligence and automation, the approach can implement. the functions of service automatic composition and model verification. These functions will improve the degree of customer-orientation and enable fast responsiveness in the emerging e-service systems.     

EXISTENCE OF 0-1 UNIVERSAL MINIMAL TOTAL DOMINATING FUNCTIONS

In this paper, we study the existence of 0-1 universal minimal total dominating functions in a graph. We establish a formulation of linear inequalities to characterize universal minimal total dominating functions and show that for a kind of graphs whose adjacent matrices are balanced, the existence of universal minimal total dominating functions coincides with that of 0-1 ones. It is also proved that for general graphs, the problem of testing the existence of 0-1 universal minimal total dominating functions is NP-hard.     

Identification of linear continuous-time system using wavelet modulating filters

An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable (Ⅳ) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.     

Novel Minimum Passive Element Realization of All-Pass Network Using A Modified Current Conveyor

1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedance attract attention, for high input impedance has the advantage that the networksmay be used in cascade without requiring impedance matching device. In the Higashimura and     

A Pragmatic Approach to Product Costing Based on Standard Time Estimation

0　 IntroductionManufacturing is often viewed as the entire process of delivering an artifact in response tocustomerneeds.Productdesign has been recognized as one ofthe mostimportantconcernsof product realization within this broad view of manufacturing,in which design decision-making implicates an overwhelming impact on many business functions,such as sales/marketing,finance and accounting,production management,logistics,and services.Theability to make design decisions is dependent upon the av…     

Using junction trees for structural learning of Bayesian networks

The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning.In this work,a novel algorithm is presented which combines ideas from local learning,constraintbased,and search-and-score techniques in a principled and effective way.It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree.Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables.Its comparison with the well-known K2 algorithm is also presented.     

Nonsmooth Semi-Infinite Minmax Programming Involving Generalized(Φ, ρ)-Invexity

This paper introduces some new generalizations of the concept of(Φ,ρ)-invexity for nondifferentiable locally Lipschitz functions using the tools of Clarke subdifferential.These functions are used to derive the necessary and sufficient optimality conditions for a class of nonsmooth semi-infinite minmax programming problems,where set of restrictions are indexed in a compact set.Utilizing the sufficient optimality conditions,the authors formulate three types of dual models and establish weak and strong duality results.The results of the paper extend and unify naturally some earlier results from the literature.     

A New Algorithm for Computing the Extended Hensel Construction of Multivariate Polynomials

This paper presents a new algorithm for computing the extended Hensel construction(EHC) of multivariate polynomials in main variable x and sub-variables u_1, u_2, ···, u_m over a number field K. This algorithm first constructs a set by using the resultant of two initial coprime factors w.r.t. x, and then obtains the Hensel factors by comparing the coefficients of x~i on both sides of an equation. Since the Hensel factors are polynomials of the main variable with coefficients in fraction field K(u_1, u_2, ···, u_m), the computation cost of handling rational functions can be high. Therefore,the authors use a method which multiplies resultant and removes the denominators of the rational functions. Unlike previously-developed algorithms that use interpolation functions or Gr?bner basis, the algorithm relies little on polynomial division, and avoids multiplying by different factors when removing the denominators of Hensel factors. All algorithms are implemented using Magma, a computational algebra system and experiments indicate that our algorithm is more efficient.     

A Fuzzy Knowledge Based System in Situation and Threat Assessment

This paper examines the use in assisting naval operators in managing the of a fuzzy knowledge-based system （KBS） situation and threat assessment problem in a littoral environment. Under the scenario created involving air contacts in a littoral environment, the assessment of the KBS model is conducted and the results show that the correlation between assessment and human operation officers＇ judgment was high. The fuzzy KBS with its corresponding toolkits is much more flexible to be integrated with other components of a command and control system, while the fine-tuning of membership functions and fuzzy inference rules are intensive, this makes any further modification a non-trivial task.     

EMERGENCE FROM LOCAL EVALUATION FUNCTION

This paper presents a new look on emergence from the aspect of locality and globality of evaluation functions for solving traditional computer problems. We first translate the Constraint Satisfaction Problem (CSP) into the multi-agent system, and then show how a global solution emerges from the system in which every agent uses a local evaluation function to decide its action, while comparing to other traditional algorithms,such as Local search and Simulated Annealing which use global evaluation functions. We also give some computer experimental results on large-scale N-queen problems and k-Coloring problems, and show that emergence only depends on problem instance, not details of agent settings, i.e. in some CSPs, the system can self-organize to a global solution~ but can not in some other CSPs no matter what settings of agents have.     

A Case of Energy and Ecology System Development and a New Approach to System Dynamics Modeling

1　IntroductionIn our previous subject concerned with the development of energy and ecology systemengineering[1 ] ,we have proposed SD rate variable fundamental in- tree modeling and,then,built the model of this system which totally included six in- trees with this newapproach.But the difficulty in locating all feedback loops is a barrier on the way of ourfurther research on the feedback structure and functions of the complex system.Therefore,a new algebraic method named after SD branch- vec…