具有不确定收益的新的投资组合优化模型的研究

解决了具有不确定收益的投资组合问题。从一个新的视角给出了不确定投资组合的风险定义,在此基础上,提出了新的投资组合优化模型,并设计出新的混合智能算法来解决这一新的优化问题。在新的算法中,99方法被用来计算期望值和机会值,与之前的算法相比,大大减少了计算的工作量,加快了求解过程。最后,提出一个数值例子来验证新的优化模型和所提算法的可行性和正确性。     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

工作流过程建模方法及模型的形式化验证

Work/low technology is widely used in business process modeling, software process modeling as well as en-terprise information integration. At present, there exist a variety of workflow modeling approaches, which differ in the easiness of modeling, expressiveness and formalism. In this paper, the modeling approaches most used in research project and workflow products are compared. And the verification of workflow model is also dealt. We argue that a ideal workflow modelin~ approach is a hybrid one, i.e. the inteuration of the above approaches.     

The models of time in simulation

The major approaches to solving the classical problems of simulation and the models of time are considered, viz., discrete-event and continuous modeling, as well as Monte-Carlo modeling. Their main propositions, advantages, shortcomings, and concrete realizations are discussed. On the basis of the conducted research, the place of the original software tool G-IPS Ultimate is shown in a series of other software products for the solution of applied simulation problems.     

Exploiting parallelism across program execution: a unificationtechnique and its analysis

A new technique for source-to-source transformation of sequential programs is described. It is shown that the transformed programs so generated provide significant speedups over the original program on vector processors and vector multiprocessors. The parallelism that arises when multiple instances of a program are executed on simultaneously available data sets is exploited. This is in contrast to the existing approaches that aim at detecting parallelism within a program. The analytic model is used to prove the optimality of the complete first policy for block selection for a class of program graphs known as nonregressive graphs. Analytic and simulation models of the technique clearly indicate the speedups that could be achieved when several data sets are available simultaneously, as is the case in many fields of interest     

Material design and development: From classical thermodynamics to CALPHAD and ICME approaches

This paper presents an overview and examples of material design and development using (1) classical thermodynamics; (2) CALPHAD (calculation of phase diagrams) modeling; and (3) Integrated Computational Materials Engineering (ICME) approaches. Although the examples are given in lightweight aluminum and magnesium alloys for structural applications, the fundamental methodology and modeling principles are applicable to all materials and engineering applications. The examples in this paper have demonstrated the effectiveness and limitations of classical thermodynamics in solving specific problems (such as nucleation during solidification and solid-state precipitation in aluminum alloys). Computational thermodynamics and CALPHAD modeling, when combined with critical experimental validation, have been used to guide the selection and design of new magnesium alloys for elevated-temperature applications. The future of material design and development will be based on a holistic ICME approach. However, key challenges exist in many aspects of ICME framework, such as the lack of diffusion/mobility databases for many materials systems, limitation of current microstructural modeling capability and integration tools for simulation codes of different length scales.     

Logic-based solution methods for optimal control of hybrid systems

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of hybrid dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete/logic part of the hybrid problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original symbolic structure of the problem (e.g., transition functions of finite state machines, logic constraints, symbolic variables, etc.) is lost during the conversion, with a consequent loss of computational performance. In this paper, we attempt to overcome such a difficulty by combining numerical techniques for solving convex programming problems with symbolic techniques for solving constraint satisfaction problems (CSP). The resulting "hybrid" solver proposed here takes advantage of CSP solvers for dealing with satisfiability of logic constraints very efficiently. We propose a suitable model of the hybrid dynamics and a class of optimal control problems that embrace both symbolic and continuous variables/functions, and that are tailored to the use of the new hybrid solver. The superiority in terms of computational performance with respect to commercial MIP solvers is shown on a centralized supply chain management problem with uncertain forecast demand.     

Hybrid client-server architecture and control techniques for collaborative product development using haptic interfaces

In this paper, a collaborative product development and prototyping framework is proposed by using distributed haptic interfaces along with deformable objects modeling. Collaborative Virtual Environment (CVE) is a promising technique for industrial product development and virtual prototyping. Network control problems such as network traffic and network delay in communication have greatly limited collaborative virtual environment applications. The problems become more difficult when high-update-rate haptic interfaces and computation intensive deformable objects modeling are integrated into CVEs for intuitive manipulation and enhanced realism. A hybrid network architecture is proposed to balance the computational burden of haptic rendering and deformable object simulation. Adaptive artificial time compensation is used to reduce the time discrepancy between the server and the client. Interpolation and extrapolation approaches are used to synchronize graphic and haptic data transmitted over the network. The proposed techniques can be used for collaborative product development, virtual assembly, remote product simulation and other collaborative virtual environments where both haptic interfaces and deformable object models are involved.     

混合系统的建模设计与仿真综述

混合系统是一类既包含连续动态行为又包含离散动态行为的系统,这类系统在实际应用中显得越来越重要,对这类系统需要探索新的模型和研究方法。该文在概述混合系统概念、特点以及发展近况的基础上,主要综述了近年来混合系统研究中的一些重要问题,指出现有各种方法的优缺点,并指出了今后的研究方向。该文首先介绍了混合系统研究中多种常见的建模方法,如混合自动机、Petri网和时段演算,然后重点讨论了混合系统一些重要性质,主要集中在对系统稳定性、可达性、可观性的分析方法上,以及混合系统的多种设计方法,并且对这些方法进行了初步评价,最后介绍了混合系统研究中一些常用的仿真工具。     

Unified problem modeling language for knowledge engineering of complex systems

The 90s has seen the emergence of hybrid configurations of four most commonly used intelligent methodologies, namely, symbolic knowledge based systems (e.g. expert systems), artificial neural networks, fuzzy systems and genetic algorithms. These hybrid configurations are used for different problem solving tasks/situations. In this paper we describe unified problem modeling language at two different levels, the task structure level for knowledge engineering of complex data intensive domains, and the computational level of the task level hybrid architecture. Among other aspects, the unified problem modeling language considers various intelligent methodologies and their hybrid configurations as technological primitives used to accomplish various tasks defined at the task structure level. The unified problem modeling language is defined in the form of five problem solving adapters. The problem solving adapters outline the goals, tasks, percepts/inputs, and hard and soft computing methods for modeling complex problems. The task structure level has been applied in modeling several applications in e-commerce, image processing, diagnosis, and other complex, time critical, and data intensive domains. We also define a layered intelligent multi-agent, operating system processes, intelligent technologies with the task structure level associative hybrid architecture. The layered architecture also facilitates component based software modeling process.Work Supported by VPAC grant no EPPNLA002.2001     

基于混合方法的供应链协同计划研究

本文主要研究了供应链协同计划中的优化问题,所建数学模型中考虑了多计划期、多产品、多供应商、多制造商和多分销中心,以供应链系统总成本和总运行时间最小化为目标,采用整数规划和仿真相结合的混合方法来求解该模型。最后通过算例说明该混合方法对多目标供应链生产-分销计划模型求解的可行性和有效性。     

A hybrid approach to minimize state space explosion problem for the solution of two stage tandem queues

Two stage open queuing networks are used for modeling the subsystem-behaviour in computers and communication networks, mass storage devices, memory servers, and queuing analysis of wireless mobile cellular networks. The queuing analysis of wireless systems is essential in order to quantify the impact of different factors on quality of service (QoS); performance measures so that wireless protocols can be designed and/or tuned in an optimal manner. In that sense two stage open queuing systems are particularly important to model handoff phenomena, especially for the integration of two different systems such as cellular and wireless local area networks (WLANs). Analytical solutions for two-dimensional Markov processes suffer from the state space explosion problem. The numerical difficulties caused by large state spaces, make it difficult to handle multiple servers at the second stage of a tandem queuing system together with server failures and repairs. This study presents a new approach to analytical modeling of open networks offering improvements in alleviating this problem. The proposed solution is a hybrid version, which combines well known spectral expansion, and hierarchical Markov reward rate approaches. Using this approach, two-stage open networks with multiple servers, break-downs, and repairs at the second stage and feedback can be modeled as three-dimensional Markov processes and solved for performability measures. Comparative results show that the new algorithm used for solution, provides a high degree of accuracy, and it is computationally more efficient than the existing approaches. The proposed model is capable of solving other three-dimensional Markov processes.     

Variational solid modeling for tolerance analysis

A variational model is a computer representation of a variational class and stands for a collection of different instances of the part or assembly modeled in CAD. The different basic approaches to variational modeling are reviewed. A surface-based approach to variational modeling is discussed. The approach is applied to solving the problems of eliminating rigid-body motion, handling incidence and tangency constraints, and modeling form variations. The application of variational modeling to automated tolerance analysis is also discussed     

Simulation of systems with dynamically varying model structure

Hybrid systems are dynamical systems composed of components with discrete and continuous behavior. Some systems change their structure during simulation, or their components behavior is essentially changing. This “structural dynamics” can be described in some modeling languages but the corresponding hybrid simulators have more or less restrictions in handling models with dynamically varying structure. Some basic approaches for the simulation of such systems are discussed in the context of modeling languages and simulators.     

基于离散粒子群算法全局QoS最优的Web服务选择

Web服务选择是Web服务组合中重要问题,也是一个NP难问题,使用离散粒子群算法对该问题进行建模求解并针对粒子群算法后期容易陷入局部最优进行改进,借鉴遗传算法中杂交变异的思想对算法迭代后期的一部分粒子进行杂交变异,实验仿真证明,由于杂交变异的引入,能够求得Web服务组合全局QoS更优的解。     

复杂陈述式仿真模型的相容性分析

模型的相容性分析是复杂产品多领域建模面临的一个关键问题.研究了陈述式基于方程仿真模型的方程系统过约束或欠约束判定、检测与修正策略.首先,将方程系统表示为二部图,通过二部图分解判定模型的相容性,并分离出方程系统的过约束和欠约束部分.然后,通过检测、判定与缩减过程,自动判别出过约束或欠约束发生的大致范围,并给出修正方案.所提出的策略与算法能够显著地提高用户发现与排除过约束或欠约束问题的效率,已在多领域物理系统混合建模与仿真平台MWorks中实现.     

Kinematics and dynamics analyses of a parallel manipulator with three active legs and one passive leg by a virtual serial mechanism

A novel CAD variation geometry approach and a virtual serial mechanism approach are proposed for analyzing the kinematics and dynamics of a parallel manipulator with three SPS-type active legs and one PU-type constrained passive leg. First, a simulation mechanism of this parallel manipulator is created, and some kinematic characteristics are analyzed. Second, the inverse formulae for solving pose and Jocabian matrix are derived, and workspace and singularity are determined. Third, a virtual serial mechanism is created, and the analytic formulae for solving active forces and constrained wrench of these parallel manipulators are derived. The analytic results are verified by using its simulation mechanism.     

Experimental comparison of parametric,non-parametric,and hybrid multigroup classification

This study evaluates the relative performance of some well-known classification techniques, as well as a proposed hybrid method. The proposed hybrid method is a combination of k-nearest neighbor (kNN) and linear programming (LP) method for four group classification. Computational experiments are conducted to evaluate the performances of these classification techniques. Monte Carlo simulation is used to generate dataset with varying characteristics such as multicollinearity, nonlinearity, etc. for the experiments. The experimental results indicate that LP approaches, in general, and the proposed hybrid method, in particular, consistently have lower misclassification rates for most data characteristics. Furthermore, the hybrid method utilizes the strengths of both methods – k-NN and linear programming – resulting in considerable improvement in the classification accuracy. The results of this study can aid in the design of various hybrid techniques that combine the strengths of different methods to improve classification accuracy and reliability.     

Modeling continuous-time processes via input-to-state filters

A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model.