同顺序（Flow—shop）排序问题的模拟退火求解

本文为Ｆｌｏｗ－ｓｈｐｏ问题的求解一般地构造一类随机方法－模拟退火算法，基于６种不同的随机抽样方式，分析表明求解算法渐近收敛于全局最优解集且具有多项式计算复杂特性，以不同实例规模的ＵＩＳ，ＦＩＳ和ＮＩＳ的Ｍａｋｅｓｐａｎ最小Ｆｌｏｗ－ｓｈｏｐ排序问题为例，计算结果表明模拟退火求解Ｆｌｏｗ－ｓｈｏｐ排序问题是有效的。     

模糊Flow—shop问题及其遗传优化

研究模糊生产环境下的Ｆｌｏｗ－ｓｈｏｐ调度问题。针对实际生产中工时间的不确定性,应用模糊加工时间参数替代传统的清晰参数表示方法,引入了一种新的模糊比较方法－－面积补偿法,构造了一种解模糊Ｆｌｏｗ－ｓｈｏｐ问题的有效遗传算法。最后给出计算实例及仿真结果。     

模糊Flow-shop问题及其遗传优化

研究模糊生产环境下的Ｆｌｏｗ－ｓｈｏｐ调度问题．针对实际生产中加工时间的不确定性,应用模糊加工时间参数替代传统的清晰参数表示方法,引入了一种新的模糊数比较方法——面积补偿法,构造了一种解模糊Ｆｌｏｗ－ｓｈｏｐ问题的有效遗传算法．最后给出计算实例及仿真结果．⒇     

一种自适应的模拟退火算法

提出一种自适应ＳＡ算法,该算法根据邻域搜索进展的反馈信息,自适应确定温度变化和邻域搜索强度。对Ｆｌｏｗ ｓｈｏｐ问题的计算结果表明,该算法基本独立于初始温度的选择,具有较强的鲁棒性。     

半导体制造中的调度

半导体生产线是带重入型的生产线,与传统 Ｊｏｂ Ｓｈｏｐ 和 Ｆｌｏｗ  Ｓｈｏｐ 生产线存在着明显区别。这使得它的调度问题成为一个难点。本文仅对近年开发的调度策略作一个简单回顾。     

一种基于犘犉犛犘性质的深度优先搜索算法

三机以上同顺序Ｆｌｏｗ ｓｈｏｐ问题（ＰＦＳＰ）是著名的ＮＰ完全问题．在充分利用ＰＦＳＰ自身特性的基础上,提出一种可变路径的深度优先搜索算法．该算法在搜索过程中根据需要采用两种不同邻域,在必要时将ＰＦＳＰ 转化为一个指派问题,自动变更搜索路径,以避免陷入局部最优解．数值仿真实验表明,该算法对于大规模ＰＦＳＰ 能取得良好
的计算结果．

     

带模糊交货期的流水生产调度问题及其模拟系统研究

建立了平行顺序移动方式下带模糊交货期的Ｆｌｏｗ Ｓｈｏｐ调度问题的模型,用每个工作模糊交货期的隶属函数表示决策者对该工件完工时间的满意度,以总满意度为目标函数,设计了一个基于遗传算法的计算机模拟系统进行仿真试验,结果是令人满意的。     

多阶段混合Flow Shop调度问题及其遗传求解算法

针对多阶段混合Ｆｌｏｗ Ｓｈｏｐ 调度问题的一般结构和不同的调度目标函数,提出混合整数规划模型,并基于问题的结构特点设计了遗传求解算法。计算实验结果表明,遗传算法对于不同规模和结构的问题具有良好的适应性和求解性能     

Flow－shop调度问题的遗传启发算法

结合遗传算法和启发式规则，构造了一种新的遗传启发搜索算法，用于求解Ｆｌｏｗ－ｓｈｏｐ调度问题．通过分析和实例计算表明，算法能够有效地适用于大规模加工过程中调度问题的优化计算，在运行时间，适应性和最优率等方面都具有很好的搜索优势．     

Flow—shop调度问题的遗传启发算法

结合遗传算法和启发式规则，构造了一种新的遗传启发搜索算法，用于求解Ｆｌｏｗ－ｓｈｏｐ调度问题。通过分析和实例计算表明，算法能够有效地适用于大规模加工过程中调度问题的优化计算，在运行时间，适应性和最优率等方面都具有很好的搜索优势。     

线性多步法模糊逻辑系统

根据求解常微分方程的线性多步逼近方法构造了线性多步法模糊逻辑系统以辨识由常微分方程所描述的未知动态系统,这种新的模糊逻辑系统在预测状态的同时也能够逼近系统微分方程中的未知函数,文中从理论上对精度作了分析,并用仿真验证了所得的结果。     

滞后复杂系统的多步预报自学习模糊控制

针对具有纯时滞及非线性的复杂系统，提出一种基于多步预报的自学习模糊控制算法，使控制效果基本不依赖于初始控制表，而控制表可以在线修正，以满足系统快速性和稳态特性的要求。仿真结果令人满意。     

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.      

Optimization of digital holographic setup by a fuzzy logic prediction system

In this study, the optimization of the digital holography setup is achieved by a using fuzzy logic prediction system. In fact, when this optimization process is experimentally performed, some parameters are changed in the setup. These parameters affect directly the obtained image quality after a reconstruction process, which is determined by normalized root mean square. The aim of this study is to achieve the optimization of digital holographic setup by using both experimental and fuzzy logic prediction systems. Furthermore, the required time during the experimental optimization can be lowered by using a numerical method like the fuzzy logic prediction system. Here, the experimental optimization results and the optimization results obtained by the fuzzy logic prediction system are compared. It is offered that the designed experimental system can be optimized by using an artificial intelligent tool. The applied fuzzy logic prediction model is used the first time for optimization of hologram recording setup. As a result, it is reached a conclusion that the optimization of digital holographic setup can be numerically performed by the fuzzy logic prediction system. Moreover, while digital holographic setup is experimentally designed, the required time for optimization is reduced, as well.     

基于直觉模糊数的GM(1,1) 预测模型

在灰色系统理论研究领域,较少见到以直觉模糊数为建模对象的预测模型的研究。鉴于此,运用直觉模糊数的记分函数和犹豫度构建直觉模糊数序列GM(1,1)预测模型,得出直觉模糊数的隶属度和非隶属度,从而实现了直觉模糊数的预测。通过算例分析表明了所提出方法的合理性和可行性,且直觉模糊数预测模型能够丰富灰色系统理论体系,扩展灰色预测模型的应用范围。     

A Stable Model-Based Fuzzy Predictive Control Based on Fuzzy Dynamic Programming

A stable model based fuzzy predictive controller based on fuzzy dynamic programming is introduced. The objective of the fuzzy predictive controller is to drive the state of the system to a terminal region where a local stabilizing controller is invoked, leading to a dual mode strategy. The prediction horizon is fixed and specified. The stability of the controlled system is studied using the value function as a Lyapunov function. Guaranteed stability is obtained under conditions on the terminal region, the local control law and the membership functions of fuzzy goal and constraints therein. The solution procedure is based on dynamic programming with branch and bound.     

Fuzzy Predictive Functional Control in the State Space Domain

In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.     

Fouling detection in a heat exchanger by observer of Takagi–Sugeno type for systems with unknown polynomial inputs

This paper proposes a new method for fouling detection in a heat exchanger. It is based on the modeling of the system in a fuzzy Takagi–Sugeno representation derived from a physical model. With this representation, the design of a fuzzy observer with unknown inputs of polynomial types is obtained via a LMI formulation. Main advantages of the proposed method are that neither specific sensor, excepted standard ones, nor special operating conditions such as steady state regime are required. Some realistic simulations show the efficiency of the proposed technique.     

Behavior-modulation technique in mobile robotics using fuzzy discrete event system

This paper presents a novel behavior-modulation technique using a fuzzy discrete event system (FDES) for behavior-based robotic control. The method exploits the multivalued feature of fuzzy logic (FL) and event-driven property of a discrete event system (DES) to generate the activity of a behavior using fuzzy state vectors. State-based prediction of an activity is accomplished using fuzzily defined event matrices. A central arbiter employs priority-based arbitration among the activity state vectors and generates new event matrices to modify the activity states of the behaviors. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensory uncertainty and environmental dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. Real-time results of FDES-based mobile robot navigation are presented and compared against four different modulation methods to validate its superior performance.     

飞机座舱压力专家模糊预控方法仿真研究

针对新型战机高空高速俯冲及俯冲转平飞情况下带来的座舱压力控制难题,提出一种飞机座舱压力专家模糊PID预控方法。在飞机高速俯冲时,基于压力调节系统时间延迟及飞机高度变化率改进常规模糊PID控制策略并提高座舱压力调整速度;在飞机状态转换时,利用专家控制器根据知识库及状态转换时间预测调整模糊PID控制策略,并引入重置机制以改善调整性能。经过知识库的动态学习,得出飞机状态转换时,采用模糊PID控制、模糊预控、重置PID控制参数的专家控制策略具有最佳的控制效果的结论。通过仿真实验验证了该方法的有效性。