机会约束下贷款组合优化决策的方差最小化模型

通过把贷款的收益率刻画为模糊变量,提出了机会约束下贷款组合优化决策的方差最小化模型。针对贷款收益率是特殊的三角模糊变量的情况,给出模型的清晰等价类,对等价类模型用传统的方法进行求解。对于贷款收益率的隶属函数比较复杂的情况,应用集成模糊模拟、神经网络、遗传算法和同步扰动随机逼近算法的混合优化算法求解模型。数值算例验证了模型和算法的有效性。     

半方差约束下的模糊随机收益率贷款组合优化模型

银行贷款的收益率在很多情况下具有模糊随机性。将贷款收益率刻画为模糊随机变量,使用半方差作为风险度量方式,建立半方差约束下的模糊随机收益率贷款组合优化模型,目的是在一定的半方差约束和置信水平下,最大化贷款组合的收益率不小于预置收益率的本原机会测度。应用集成模糊随机模拟、神经网络、遗传算法的混合智能算法进行求解,最后通过实例验证了模型和算法的可行性和有效性。     

具有模糊资金需求的银行卡网络决策模型

在分析国内银联网络资金需求情况的基础上,采用模糊变量表示各银行现金和非现金需求的不确定性,建立了以银行卡网络成本最小为目标的决策模型。当资金需求刻画为三角模糊变量时,模型可转化成清晰等价形式,因而可以采用传统的优化算法求解;当资金需求刻画为一般模糊变量时,应用混合智能算法求解。最后,给出一个案例,在不同的置信水平下仿真计算,结果表明模型是有效的。     

带有最小风险准则的两阶段模糊运输模型

基于可信性理论和两阶段模糊优化方法,提出一类新的带有最小风险准则的两阶段模糊运输模型。由于提出的模糊运输问题包含带有无限支撑的模糊变量参数,因此它是一个无限维的优化问题。为了求解这个模糊优化问题,这里将讨论两阶段模糊运输问题的逼近方法并且将逼近方法嵌套到遗传算法中产生一个基于遗传算法的混合智能算法求解提出的带有最小风险准则的两阶段模糊运输问题。给出一个数值例子来表明所设计模型和算法的实用性。     

具有不确定次品率的EPQ模型及其求解算法

假设在具有衰变特性的生产过程中次品率为随机变量或模糊变量的情形下,分别建立了经济生产批量模型;给出了次品率为随机变量情形下最优经济生产批量的解析表达式;设计了模糊模拟算法以及基于模糊模拟的粒子群优化算法对次品率为模糊变量情形下的经济生产批量模型进行求解。最后给出了两种情形下的数值实例来说明模型的求解过程以及所设计算法的有效性。     

广义多变量模糊C均值聚类算法

模糊聚类算法为了保证算法的收敛性,要求模糊指标m取值大于1,这限制了算法的普适性。提出广义多变量模糊C均值聚类算法(GMFCM),在多变量模糊C均值聚类算法(MFCM)的基础上,利用粒子群优化算法对分量模糊隶属度进行优化估计,进而将模糊指标拓展到m>0的情况,同时采用梯度法得到算法聚类中心迭代公式。GMFCM理论分析了模糊指标m扩展的原理,研究了模糊指标m在不同取值情况下的性质,解释了模糊指标m的实际意义,讨论了GMFCM算法的收敛性。GMFCM继承了MFCM算法的样本分量区分性能,弥补了MFCM算法聚类中心分量与样本分量重合时的不完备性,突破了模糊聚类算法对参数m的约束,提高了模糊聚类算法的普适性。基于gauss数据集和UCI数据集的仿真测试验证了所提算法的有效性。     

基于模糊理论的最优序贯拍卖策略研究

基于模糊理论,通过将之前拍卖的类似物品回报进行模糊参数化,采用均值—方差对其进行收益和风险的刻画和度量。针对这两个准则提出基于柯布—道格拉斯生产函数的多准则优化函数,进而构建了基于模糊理论的序贯拍卖的顺序策略优化模型;其次,通过集成模糊模拟算法和多准则0-1遗传算法,用于求解该顺序策略优化模型;最后,算例分析比较了五种顺序策略以及优化策略,显示通过模型求解得到的最优策略能够以较低风险取得较高收益。     

FCMBP模糊聚类算法的改进

在FCMBP算法中,高阶模糊等价标准型的平移等价类数据库缺少一个高效的生成算法,且每一个模糊等价标准型的平移等价类需要定义相应的相似参数系等价类,过程繁琐。为解决上述问题,提出由低阶向高阶自动生成模糊等价标准型矩阵的平移等价类数据库的算法以及生成相应相似参数系的等价类的算法。通过实例验证该算法较好地解决了高阶模糊等价标准型的平移等价类数据库的自动生成问题。     

面向云环境的集群资源模糊聚类划分算法的优化

传统的串行模糊聚类分析算法在应对高维矩阵运算时存在运算量大、运算效率低等问题,难以满足云环境中集群资源调度的时效性要求。为此,在基于等价关系的模糊聚类算法基础上对传递闭包法进行优化,提出一种基于多线程的云资源模糊聚类划分并发算法,并将其应用于Hadoop调度器的策略改进。仿真实验结果表明,优化策略有助于减少平方法求解模糊等价矩阵的计算量,所设计的并发算法能够有效解决中小规模云集群资源聚类的运算瓶颈问题,且具有较好的加速比。为了解决现有Hadoop调度器存在的异构性问题,对该优化并发算法进行了理论分析,结果表明它有助于解决异构性带来的调度难题。     

基于混合聚类算法的模糊函数系统辨识方法

针对传统模糊系统存在的结构难以确定和参数辨识复杂的问题,提出了一种基于混合聚类算法的模糊函数系统辨识算法.与一般的模糊函数系统相比,混合聚类算法结合模糊C均值和模糊C回归模型聚类算法的样本距离.在模型预测部分,采用高斯函数计算每个输入变量的隶属度,利用输入变量隶属度的模糊化算子得到输入向量的隶属度.应用于Box-Jenkins煤气炉数据、一个双入单出的非线性系统和Mackey-Glass混沌时间序列数据的试验结果表明,本文算法具有很好的辨识效果,从而验证了本文算法的有效性与实用性.     

A solid transportation problem with type-2 fuzzy variables

This paper focuses on generating the optimal solutions of the solid transportation problem under fuzzy environment, in which the supply capacities, demands and transportation capacities are supposed to be type-2 fuzzy variables due to the instinctive imprecision. In order to model the problem within the framework of the credibility optimization, three types of new defuzzification criteria, i.e., optimistic value criterion, pessimistic value criterion and expected value criterion, are proposed for type-2 fuzzy variables. Then, the multi-fold fuzzy solid transportation problem is reformulated as the chance-constrained programming model with the least expected transportation cost. To solve the model, fuzzy simulation based tabu search algorithm is designed to seek approximate optimal solutions. Numerical experiments are implemented to illustrate the application and effectiveness of the proposed approaches.     

Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty

This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.     

A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems

The Markowitz’s mean-variance (M-V) model has received widespread acceptance as a practical tool for portfolio optimization, and his seminal work has been widely extended in the literature. The aim of this article is to extend the M-V method in hybrid decision systems. We suggest a new Chance-Variance (C-V) criterion to model the returns characterized by fuzzy random variables. For this purpose, we develop two types of C-V models for portfolio selection problems in hybrid uncertain decision systems. Type I C-V model is to minimize the variance of total expected return rate subject to chance constraint; while type II C-V model is to maximize the chance of achieving a prescribed return level subject to variance constraint. Hence the two types of C-V models reflect investors’ different attitudes toward risk. The issues about the computation of variance and chance distribution are considered. For general fuzzy random returns, we suggest an approximation method of computing variance and chance distribution so that C-V models can be turned into their approximating models. When the returns are characterized by trapezoidal fuzzy random variables, we employ the variance and chance distribution formulas to turn C-V models into their equivalent stochastic programming problems. Since the equivalent stochastic programming problems include a number of probability distribution functions in their objective and constraint functions, conventional solution methods cannot be used to solve them directly. In this paper, we design a heuristic algorithm to solve them. The developed algorithm combines Monte Carlo (MC) method and particle swarm optimization (PSO) algorithm, in which MC method is used to compute probability distribution functions, and PSO algorithm is used to solve stochastic programming problems. Finally, we present one portfolio selection problem to demonstrate the developed modeling ideas and the effectiveness of the designed algorithm. We also compare the proposed C-V method with M-V one for our portfolio selection problem via numerical experiments.     

不确定条件下配送回收中心选址配送问题研究

针对制造商、零售商、一个废弃处理中心和多个配送回收中心构成的闭环供应链,解决模糊随机环境下的配送回收中心选址配送问题。引用模糊随机理论处理产品回收率和可再利用率随机变量,以成本最低和碳排放最小为双重目标,以设施能力,设施间流量以及设施数量为约束,建立多目标闭环供应链配送回收中心选址配送模型。改进了全局-局部-邻域粒子群算法,设计了基于优先级的全局-局部-邻域粒子群算法方案,并用案例验证了模型及算法的有效性和先进性。     

Structured Learning and Decomposition of Fuzzy Models for Robotic Control Applications

In this paper a specially designed structured-optimization procedure is used for learning the parameters of the Takagi–Sugeno (TS) type fuzzy models. It is well-known that the number of learning parameters increases exponentially with the number of model inputs. Therefore an appropriate learning scheme with preliminary structuring of the learning parameters into two groups: antecedent parameters and consequent parameters can be helpful for speeding-up the learning process. Two different optimization algorithms for tuning the antecedent and consequent parameters respectively are used in a sequence of repetitive loops (epochs). The stop criterion is defined as a number of repetitions of the loops or as a desired minimal error. Random walk algorithm with variable step size is used in this paper for tuning the antecedent parameters of the membership functions. For tuning the consequent parameters of the singletons, a specially proposed local learning algorithm is used. The problem of dimensionality reduction in fuzzy modeling is also considered in the paper from another viewpoint, namely as a hierarchical fuzzy model structure. It is accomplished by a decomposition of the complete fuzzy model into a feedforward hierarchical structure of sub-models called partial fuzzy models each one with two inputs and one output. Then the local models are learned separately in a preliminary specified and repetitive order. Such decomposition scheme has a potential for a significant reduction of the number of model parameters to be tuned thus reducing the total learning time. It has been experimentally shown that both concepts for dimensionality reduction in learning fuzzy models have benefits in learning speed and accuracy. A comparison with simultaneous optimization of all parameters of a single fuzzy model is also given. It shows that the proposed structured learning as well as the decomposition of the fuzzy model into a hierarchical fuzzy model structure lead to reducing the learning time and creating more accurate fuzzy models. Finally an application for learning a fuzzy controller of a two-link robot motion is shown and analysed.     

Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems

This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.     

Expected model for portfolio selection with random fuzzy returns

This paper researches portfolio selection problem in combined uncertain environment of randomness and fuzziness. Due to the complexity of the security market, expected values of the security returns may not be predicted accurately. In the paper, expected returns of securities are assumed to be given by fuzzy variables. Security returns are regarded as random fuzzy variables, i.e. random returns with fuzzy expected values. Following Markowitz's idea of quantifying investment return by the expected value of the portfolio and risk by the variance, a new type of mean–variance model is proposed. In addition, a hybrid intelligent algorithm is provided to solve the new model problem. A numeral example is also presented to illustrate the optimization idea and the effectiveness of the proposed algorithm.     

Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty

Absolute deviation is a commonly used risk measure, which has attracted more attentions in portfolio optimization. The existing mean-absolute deviation models are devoted to either stochastic portfolio optimization or fuzzy one. However, practical investment decision problems often involve the mixture of randomness and fuzziness such as stochastic returns with fuzzy information. Thus it is necessary to model portfolio selection problem in such a hybrid uncertain environment. In this paper, we employ random fuzzy variable to describe the stochastic return on individual security with ambiguous information. We first define the absolute deviation of random fuzzy variable and then employ it as risk measure to formulate mean-absolute deviation portfolio optimization models. To find the optimal portfolio, we design random fuzzy simulation and simulation-based genetic algorithm to solve the proposed models. Finally, a numerical example for synthetic data is presented to illustrate the validity of the method.