含分段线性隶属函数的模糊规划建模方法

针对具有分段线性隶属函数的模糊规划问题,提出了一种新的模糊规划建模方法。通过对二元变量的合理配置,准确地表达出了隶属函数的各分段之间的关系。这种方法可以有效地避免常规建模方法解决模糊规划时出现的系统失去约束的问题,使得优化问题能够获得满意的最优解。并且,在一些情况下可以使用比普通方法更少的二元项进行优化问题的建模,从而使得计算更为简便。详细地给出了两种相关的建模方法,并通过实例进行了验证。     

第一类模糊非线性规划解

本文讨论了第一类模糊非线性规划的解法,提供了采用摄动可行方向法求解该类模糊规划的BASIC程序,并在IBM PC/XT电子计算机上认证通过。特别地,该程序也适用于求解第一类模糊线性规划。     

基于模糊预测型线性规划模型的矿山产能配置

将线性规划与模糊数学及灰色理论有机结合,构建了与产能分配实际情况较为接近的模糊预测型线性规划模型.利用灰色预测理论对各灰色系数进行白化,将模糊预测型线性规划模型转化为模糊线性规划模型,利用最优判决条件进一步转化,得到以最大隶属度为目标函数的一般线性规划模型,求解得到了矿山的最优产能分配,以实现矿山产能的科学配置和效益的最大化.     

基于自适应模糊推理的非线性系统辨识器设计

针对传统模糊建模方法中模型参数都是根据经验选取的局限性,提出一种类高斯隶属函数,推导了基于类高斯隶属函数的自适应模糊推理模型,利用Stone-Weierstrass定理证明了该模型能以任意精度逼近非线性系统.将自适应模糊推理模型应用于非线性动态系统辨识中,设计了非线性系统辨识器,采用梯度下降算法学习模型中参数,通过仿真得到了较好的辨识效果.     

板翅式换热器模糊优化设计

应用结构模糊优化理论对板翅式换热器进行多目标优化设计；给出了构造隶属函数及确定目标函数权重的方法，通过线性加权法将多目标化为单目标求解。应用此软件设计了一台换热器，其实际使用结果表明完全达到设计要求。     

化工企业生产计划优化中非线性单耗的建模方法

化工过程生产装置的原料单耗与生产负荷呈明显的非线性关系。为了得到更切合实际的化工企业生产计划优化结果,采用分段线性函数和多项式函数对非线性单耗问题建模并进行比较。案例结果表明,分段线性函数不需要回归参数,建模简单,精度较高,对于中等规模问题求解时间短,能够同时处理装置的多种原料非线性单耗,而不需要增加新的整数变量,多项式函数则不具有这些优点。分段线性函数建模方法已经在化工企业生产计划图形建模优化系统(GIOCIMS)中实现,并在中国石化巴陵分公司得到应用。     

环境因子对发酵过程影响潜力预测

提出一种预测环境因子pH、温度和溶氧对酵母发酵影响潜力的方法。在构造环境因子对发酵过程评价的隶属函数的基础上 ,建立由模糊评价向量、隶属度矩阵和因子权重向量构成的模糊关系方程模型。经实验确定模糊评价向量和隶属度矩阵 ,通过求解模糊关系方程得到因子权重向量 ,预测环境条件因子对发酵的影响潜力。结果令人满意。     

一种新型的模糊CMAC网络的研究及应用

描述一种新型的模糊CMAC网络，运用全新过程的可变感受野来代替固定感受野，同时采用规则启动通道层代替原有的哈稀散列层，从而避开哈稀散列表的冲突问题。网络模型的隶属度函数采用且角形函数，参数学习采用改进的进化规划算法，并提出递归变化率的概念。存储区权值采用最小二乘法的批学习方法进行求解。     

改进粒子群模糊控制算法在注塑机电液比例控制系统的应用

针对精密注塑机对注塑闭环控制效果要求较高的问题,依据流体学传动原理阐述了数学模型,该模型以伺服比例阀为核心控制元件,因为系统的非线性与熔融负载的不确定性,利用模糊算法最终确定了系统的非线性微分方程。引入粒子群算法改进模糊控制算法的隶属度函数与模糊规则,对注塑过程的电液比例控制系统的参数进行优化,通过对系统的Matlab仿真,有效地验证了算法的有效性,大大提高了系统的鲁棒性和灵敏性。     

基于模糊非线性回归的煤炭发热量预测研究

针对经典线性回归模型无法反映变量间的非线性关系,不适宜预测有模糊数的煤炭发热量的问题,提出了一种基于三角模糊数的多元非线性回归的煤炭发热量预测模型。以我国新疆伊犁地区煤炭工业分析为建模数据和模型检验数据,将计算模糊中心值和模糊幅度值的问题转化为约束非线性优化问题,采用MATLAB优化工具箱求解。最后对比分析了模糊非线性回归、经典线性回归、BP(Back Propagation)神经网络及支持向量机回归4种模型对测试煤样发热量的预测结果。结果表明,模糊非线性回归模型的线性拟合优度值为0.9997,调整后的非线性拟合优度值为0.9838,均方误差为0.4473;测试煤样的平均相对误差为0.0203,80%的测试煤样模糊隶属度大于0.5。模糊非线性回归模型具有很高的精确度和可靠性,可用来预测预报煤炭发热量。     

An Approach to Formulation of FNLP with Complex Piecewise Linear Membership Functions

Traditionally, extra binary variables are demanded to formulate a fuzzy nonlinear programming （FNLP） problem with piecewise linear membership functions （PLMFs）. However, this kind of methodology usually suffers increasing computational burden associated with formulation and solution, particularly in the face of complex PLMFs. Motivated by these challenges, this contribution introduces a novel approach free of additional binary variables to formulate FNLP with complex PLMFs, leading to superior performance in reducing computational complexity as well as simplifying formulation. A depth discussion about the approach is conducted in this paper, along with a numerical case study to demonstrate its potential benefits.     

基于多目标的模糊神经网络及在pH控制过程中的应用

设计了一种基于多目标的动态模糊递归神经网络（FRNN）建模方法,用于pH中和过程的广义预测控制。所设计的多目标优化算法以提高拟合精度和简化网络结构为原则,同时优化模糊神经网络中的模糊规则数、隶属度函数中心点及其宽度,由此得到的FRNN模型可以高精度拟合pH中和过程。依据该动态模型,在控制过程的每一个控制周期得到其局部线性模型,将广义预测控制中复杂的非线性优化问题转化为简单的二次线性规划问题。仿真对比结果验证了所提方法的有效性。     

Impact of Fuel Cell Power Plants on Multi‐objective Optimal Operation Management of Distribution Network

This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO‐DE) to solve the multi‐objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi‐objective problem by assuming that the decision‐maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

From graphical to model-based distillation column design: A McCabe-Thiele-inspired mathematical programming approach

We propose a mixed-integer nonlinear programming (MINLP) model for simple and complex distillation column design and optimization. The model is based upon the concepts and equations underpinning the McCabe-Thiele method. Generalizing this method, we introduce material balances at various locations of the column and employ binary variables to determine the optimal number of trays and optimal feed locations. We model the vapor–liquid equilibrium using continuous piecewise linear approximating functions. The model is extended to account for multicomponent mixtures and non-constant-molar overflow. We also discuss how to estimate the minimum number of trays and the minimum reflux ratio.     

需求不确定下炼油企业生产计划优化混合规划模型

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.     

Comparison of global optimization algorithms for the design of water-using networks

We address a special class of bilinear process network problems with global optimization algorithms iterating between a lower bound provided by a mixed-integer linear programming (MILP) formulation and an upper bound given by the solution of the original nonlinear problem (NLP) with a local solver. Two conceptually different relaxation approaches are tested, piecewise McCormick envelopes and multiparametric disaggregation, each considered in two variants according to the choice of variables to partition/parameterize. The four complete MILP formulations are derived from disjunctive programming models followed by convex hull reformulations. The results on a set of test problems from the literature show that the algorithm relying on multiparametric disaggregation with parameterization of the concentrations is the best performer, primarily due to a logarithmic as opposed to linear increase in problem size with the number of partitions. The algorithms are also compared to the commercial solvers BARON and GloMIQO through performance profiles.     

自适应模糊滑模控制在化工过程中的应用

为有效处理多变量、非线性及非最小相位系统的复杂化工过程,提出了一种新型的自适应模糊滑模控制,该方法针对滑模控制鲁棒性好但存在抖振的问题,采用模糊控制柔化控制信号,而与滑模控制的结合可以充分利用系统信息,简化模糊控制;在此基础上提出一种新的自适应调整比例因子来进行模糊变论域,柔化了控制信号并减小了滑模控制器输出的抖振。并给出模糊滑模控制的算法和稳定性分析,得到简化后的通用模糊规则库,可通过比例因子在线调节输入量的论域,使构成的控制系统具有很强的鲁棒性、较好的自适应能力和较高的控制精度。最后对于非线性单输入单输出(SISO)和多输入多输出(MIMO)化工模型进行仿真研究,结果表明即使工况点发生大的变化或受到较大干扰时,仍具有良好的抗扰动能力和很强的鲁棒性。     

MODELING SIMULATION AND CONTROL OF THE WOOD DRYING PROCESS Part 3. Programming and Computer Solutions

In this paper, the programming and computer subroutines of wood drying process (WDP) are studied. This WDP is represented by the set of non-linear partial differential equations which are decomposed into fast and slow subsystems. Moreover, a few procedures have been used to estimate piecewise linear parameters and to reconstruct the real parameter distributions along the drying kiln. Several aspects including estimating stepsize, tuning sensitivity of the operating functions, and general solutions of the internal model (M) are discussed and illustrated