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。     

商业银行贷款组合优化决策的机会准则模型

通过把贷款的收益率刻画为模糊变量,提出了商业银行贷款组合优化决策的机会准则模型,即可能性准则模型、必要性准则模型和可信性准则模型。对于贷款收益率是特殊的三角模糊变量的情况,给出了模型的清晰等价类,这些等价类可以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。     

模糊机会约束数据包络分析模型及其应用

为求解模糊环境下输出倾向的数据包络分析（DEA）模型,利用可信性测度建立了一类新的输出倾向的可信性DEA（CDEA）模型,其中目标函数采用了模糊机会约束规划的概念,且所有的约束条件中都含有模糊输入和模糊输出数据。当模糊输入和模糊输出数据为相互独立的梯形模糊变量时,把所建立的CDEA模型转化为其清晰等价形式,进而研究了模型的两个基本性质;通过一个应用实例来说明所建立CDEA模型的有效性。     

PSO优化在模糊联合补充问题中的应用

对用PSO算法解决需求为不确定的联合补充问题进行了研究。运用模糊规划方法处理需求为模糊变量的联合补充问题,得到了作为求解目标的模糊数学模型;采用PSO思想对该模型进行分析,转化为PSO问题模型,制定出算法流程,并用数值实例验证了提出的粒子群优化模型和求解算法的有效性;对随机生成的大量数据进行处理,结果证明问题规模相同时该算法较遗传算法具有更高的效率。     

软件需求权重的模糊网络分析法

针对软件开发中顾客需求元素间的复杂关系,分析了层次分析法（AHP）与模糊层次分析法（FAHP）在解决复杂需求权重时所存在的缺陷;运用网络分析法（ANP）与梯形模糊数相结合的模糊网络分析法,建立加权极限超矩阵求解需求元素的混合权重;对比分析了模糊网络分析法、模糊层次分析法和网络分析法的权重结果;揭示了在复杂系统中元素的相互联系和顾客需求相对重要度评价的模糊性在权重的求解中的重要性;验证了模糊网络分析法在复杂需求权重求解中的可行性。研究结果为软件开发中顾客需求复杂权重的准确求解提供了依据。     

矿山产能分配的变量模糊线性规划模型

针对矿山资源开采过程中产能不确定的分配问题,引入了模糊结构元素理论。将产能用结构元表示,并利用结构元加权序将模糊数比较转化为单调函数比较,将含有模糊变量的线性规划问题等价转化为经典线性规划问题。以某矿山为例,建立矿山产能分配的变量模糊线性规划模型,并进行求解。结果表明：实现了将实际问题中的模糊事件进行精确表达,原问题的求解更简便。得到矿山产能取得最大可能利润时的可能分配。应用结构元加权序求解的线性规划模型优于结构元元序的。     

考虑区间约束的物流网络双层规划模型及算法

考虑物流网络需求的不确定性,利用区间参数度量不确定性变量与参数,建立区间需求模式下的物流网络双层规划模型,设计了一种含区间参数与变量的递阶优化遗传算法,通过定义问题求解的风险系数与最大决策偏差,给出适合物流网络结构的区间运算准则,实现模型的确定性转化。以区间松弛变量与0-1决策变量定义初始种群,通过两阶遗传操作运算,求解不同情景下双层规划目标的区间最优解与节点决策方案。算例测试表明算法求解的可操作性更强,求解结果具有区间最优解与情景决策的优越性。     

模糊网络博弈的合作联盟优化对策研究

模糊网络博弈主要关注如何将联盟收益分配给合作联盟的每个参与者,其广义模糊博弈解同时引入参与度和调整系数,不仅实现了参与者以部分资源参与合作联盟的愿望,而且满足了保留部分收益值用于联盟再发展的需求.本文作为模糊博弈模型的后续深入研究,对模糊网络博弈的解进行拓展,提出广义模糊核心解、广义模糊谈判集解的概念,并证明当满足超可加性的前提下,模糊网络博弈的广义模糊核心解与其广义模糊谈判集解具有等价关系,并刻画了模糊网络博弈广义核心解的非空性,算例分析结果表明合作联盟广义分配方案的存在性,为合作联盟优化对策提供服务.     

差分进化算法在模糊多资源约束联合补货模型中的应用

本文用三角模糊数表示不确定的资金约束,用梯形模糊数表示不确定的存储空间约束,构建了模糊规划联合补货模型,目标函数为最小化订货成本、库存持有成本和运输成本,决策变量为基本补充周期和每种产品的补充周期。通过对变异算子与选择操作进行变化,设计了改进的差分进化算法对模型进行求解,并通过实例证实了模型与算法的科学合理性。     

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。     

Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network

In order to predict the service life of large centrifugal compressor impeller correctly, the rough set and fuzzy Bandelet neural network are combined to construct the novel prediction model which can give full play to theirs advantages. The attribute reduction algorithm based rough set and clustering method is firstly designed to optimize the inputting variables of fuzzy Bandelet neural network. And then the prediction model based on fuzzy Bandelet neural network is proposed, the Bandelet function is used as the excitation function of hidden layer and is combined with fuzzy theory to improve the prediction effectiveness of the prediction model. The training algorithm of fuzzy Bandelet neural network is designed based on improved genetic algorithm, the improved genetic algorithm introduces the adaptive differential evolution method into the traditional genetic algorithm, which can effectively optimize the parameters of fuzzy Bandelet neural network. Finally, the original 30 input variables of fuzzy Bandelet neural network are reduced to 9 input nodes based on rough set using 500 remanufacturing impellers as research objects. The service life of remanufacturing impeller is predicted based on three prediction models, and simulation results show that the fuzzy Bandelet neural network optimized by improved genetic algorithm has highest prediction precision and efficiency, which can correctly predict the service life of remanufacturing impeller.     

A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

An important issue, when shipping cost and customers demand are random fuzzy variables in supply chain network (SCN) design problem, is to find the network strategy that can simultaneously achieve the objectives of minimization total cost comprised of fixed costs of plants and distribution centers (DCs), inbound and outbound distribution costs, and maximization customer services that can be rendered to customers in terms of acceptable delivery time. In this paper, we propose a random fuzzy multi-objective mixed-integer non-linear programming model for the SCN design problem of Luzhou Co., Ltd. which is representative in the industry of Chinese liquor. By the expected value operator and chance constraint operator, the model has been transformed into a deterministic multi-objective mixed-integer non-linear programming model. Then, we use spanning tree-based genetic algorithms (st-GA) by the Prüfer number representation to find the SCN to satisfy the demand imposed by customers with minimum total cost and maximum customer services for multi-objective SCN design problem of this company under condition of random fuzzy customers demand and transportation cost between facilities. Furthermore, the efficacy and the efficiency of this method are demonstrated by the comparison between its numerical experiment results and those of tradition matrix-based genetic algorithm.     

Fuzzy random multi-objective optimization based routing for wireless sensor networks

Wireless sensor networks are deployed in complex and uncertain environments, and multiple objectives of routing algorithms are expected to be optimal. However, routing algorithms based on deterministic single objective optimization may not flexibly meet the above needs of applications. This paper adopts fuzzy random optimization and multi-objective optimization, introduces fuzzy random variables to describe both fuzziness and randomness of link delay, link reliability and nodes’ residual energy, and proposes a routing model based on fuzzy random expected value and standard deviation model. A hybrid routing algorithm based on fuzzy random multi-objective optimization is designed, which embeds fuzzy random simulation into genetic algorithm with Pareto optimal solution. Simulation results show that the presented algorithm, by adjusting the parameters of fuzzy random variables for depicting both fuzziness and randomness, achieves a longer lifetime and wider performances of delay, latency jitter, reliability, communication interference, energy and balanced energy distribution. Therefore, the presented algorithm can meet different application needs of the cluster head network in the two-tiered wireless sensor networks.     

基于尖峰自组织模糊神经网络的需水量预测

短期需水量预测是城市给水管网安全稳定运行的前提和保证.针对日需水量预测提出一种基于尖峰机制的自组织模糊神经网络(SSOFNN)模型.针对影响变量复杂多变的特点,采用主成分分析对原始数据进行降维处理,获取线性无关的主成分变量作为预测模型输入数据.SSOFNN模型根据尖峰强度和误差指标在训练过程中对隐含层神经元进行增长修剪,结合改进Leveberg-Marquardt算法简化参数更新过程中的计算过程,大大减少了计算量,能够获得紧凑的网络结构,且跟踪精度高,运行时间短,预测效果好.     

Fuzzy two-stage material procurement planning problem

Material procurement planning (MPP) deals with the problem that purchasing the right quantity of material from the right supplier at the right time, a purchaser can reduce the material procurement costs via a reasonable MPP model. In order to handle the MPP problem in a fuzzy environment, this paper presents a new class of two-stage fuzzy MPP models, in which the material demand, the spot market material unit price and the spot market material supply quantity are assumed to be fuzzy variables with known possibility distributions. In addition, the procurement decisions are divided into two groups. Some procurement decisions, called first-stage decisions, must be taken before knowing the the particular values taken by the fuzzy variables; while some other decisions, called second-stage decisions, can be taken after the realizations of the fuzzy variables are known. The objective of the proposed fuzzy MPP model is to minimize the expected material procurement costs over the two stages. On other hand, since the fuzzy material demand, the fuzzy spot market material unit price and the fuzzy spot market material supply quantity are usually continuous fuzzy variables with infinite supports, the proposed MPP model belongs to an infinite-dimensional optimization problem whose objective function cannot be computed exactly. To avoid this difficulty, we suggest an approximation approach (AA) to evaluating the objective function, and turn the original MPP model into an approximating finite-dimensional one. To show the credibility of the AA, the convergence about the objective function of the approximating MPP model to that of the original MPP one is discussed. Since the exact analytical expression for the objective function in the approximating fuzzy MPP model is unavailable, and the approximating MPP model is a mixed-integer program that is neither linear nor convex, the traditional optimization algorithms cannot be used to solve it. Therefore, we design an AA-based particle swarm optimization to solve the approximating two-stage fuzzy MPP model. Finally, we apply the two-stage MPP model to an actual fuel procurement problem, and demonstrate the effectiveness of the designed algorithm via numerical experiments.     

Designing neurofuzzy system based on ICART algorithm and its application for modeling jet fuel endpoint of hydrocracking process

In this paper, a neuro-fuzzy system based on improved CART algorithm (ICART) is presented, in which the ICART algorithm is used to design neuro-fuzzy system. It is worth noting that ICART algorithm partitions the input space into tree structure adaptively, which avoids the curse of dimensionality (number of rules goes up exponentially with number of input variables). Moreover, it adopts density function to construct the local model for every node in order to overcome the discontinuous boundaries existed in CART algorithm. In addition, a supervised scheme is used to adjust parameters to minimize the network output error and construct more accurate fuzzy model on the basis of the ICART algorithm. Finally, to illustrate the validity of the proposed method, a simulation research and a practical application are done. The results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling, possesses high learning efficiency, and is smoother than CART algorithm. It can be successfully applied to modeling jet fuel endpoint of hydrocracking processing.     

基于模糊智能模型的QoS路由算法研究

说明了利用模糊智能方法来实现QoS路由算法的原因和优点，给出模糊路由算法模型并加以解释，通过软件仿真以及将模糊路由算法和静态，动态最短路由算法作比较，说明了模糊QoS路由算法的优越性。     

一种基于模糊逻辑的Hop-by-Hop实时流传输控制机制研究与设计

差分服务被证明是Internet网中引入服务质量保证(QoS)有效的解决方案.分析了已有的一些流控制方式,在差分服务的框架下提出一种基于逐跳技术和模糊逻辑来保证实时流传输需求和提高网络效率的机制,重点阐述了其行为逻辑和模糊判别机制.该机制通过模糊逻辑对路由器当前的情况进行判别,利用逐跳过程调整输入速率.采用NS2对该机制进行了模拟实现和评估,结果表明相比较于传统的端到端控制方法在带宽利用、延迟控制方面有明显提高.     

D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process

A soft-sensor modeling method based on dynamic fuzzy neural network (D-FNN) is proposed for forecasting the key technology indicator convention velocity of vinyl chloride monomer (VCM) in the polyvinylchloride (PVC) polymerizing process. Based on the problem complexity and precision demand, D-FNN model can be constructed combining the system prior knowledge. Firstly, kernel principal component analysis (KPCA) method is adopted to select the auxiliary variables of soft-sensing model in order to reduce the model dimensionality. Then a hybrid structure and parameters learning algorithm of D-FNN is proposed to achieve the favorable approximation performance, which includes the rule extraction principles, the classification learning strategy, the precedent parameters arrangements, the rule trimming technology based on error descendent ratio and the consequent parameters decision based on extended Kalman filter (EKF). The proposed soft-sensor model can automatically determine if the fuzzy rules are generated/eliminated or not so as to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. Model migration method is adopted to realize the on-line adaptive revision and reconfiguration of soft-sensor model. In the end, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical-and-economic indexes and satisfy the real-time control requirements of PVC polymerizing production process.