基于Hill理论及模糊算法的骨骼肌力建模与仿真

目的:针对骨骼肌力的产生过程,基于Hill肌肉收缩理论及模糊算法,对骨骼肌力进行了计算机建模及仿真,为相关领域的研究与应用提供了一个肌力计算平台。方法:根据Hill肌肉收缩理论,建立肌力数学模型,并结合模糊控制算法,建立骨骼肌力的模糊数学模型;在Matlab环境下,实现了基于Hill理论及模糊算法的肌力建模与仿真计算平台。结果:经实验验证,肌力仿真计算结果与EMG信号描述一致,并给出了精确的肌力计算值。结论:所提出的肌力预测模型为人体骨肌系统的动力学计算提供了一种新的肌力计算方法,具有一定的创新价值。

汽车磁流变半主动悬架的模糊控制

为了提高汽车的舒适性和安全性,针对汽车磁流变半主动悬架的控制策略进行研究;首先采用模糊理论算法设计控制器对其进行控制;其次,在Simulink中对模糊控制系统进行仿真,验证控制策略的可行性。仿真实验结果表明:此种模糊控制的半主动悬架能够有效地提高车辆悬架的整体性能。与被动悬架相比,在随机路面激励情况下磁流变半主动悬架的车身加速度和悬架动行程均有明显提升。

一种基于EKFCM算法的电站脱硫系统目标工况库的建立方法

在对电站湿法烟气脱硫系统运行特性分析的基础上,建立了以系统运行成本为目标的综合评价标准,给出了从系统历史数据库中建立系统目标运行工况库的基本流程。提出信息熵理论结合K均值和模糊C均值的EKFCM算法,采用信息熵跃迁值差值来评价系统信息量有序程度,并以K均值聚类结果作为初始条件,并完成测试数据的聚类分析,测试结果表明该算法具有较高精度。以某600 MW机组石灰石/石膏湿法烟气脱硫系统为对象,按照负荷和入口烟气SO2浓度将运行工况划分为多个工况簇,以p H值、液气比和浆液密度等可控参数为聚类输入,以单位SO2脱除成本为工况评价标准,从系统历史运行数据中聚类出各个簇内的目标工况,并建立了目标工况模型,得到了连续的最优目标工况库,可为现场运行人员提供实际参考。

模糊理论在遗传算法中的运用

遗传算法的寻优能力和收敛速度与许多因素有关,其间的关系复杂且难以精确描述。模糊理论能很好地描述这种特性。本文分析了影响遗传算法寻优能力和收敛速度的多种因素,运用模糊理论指导遗传算法中交叉和变异概率的自适应调整,以及改善局部寻优能力。实验结果表明,此法是有效可行的。

Multiple vehicle routing problem integrated reverse logistics with fuzzy reverse demands

A new type of vehicle routing problem （VRP）, multiple vehicle routing problem integrated reverse logistics （MVRPRL）, is studied. In this problem, there is delivery or pickup （or both） and uncertain features in the demands of the clients. The deliveries of every client as uncertain parameters are expressed as triangular fuzzy numbers. In order to describe MVRPRL, a multi-objective fuzzy programming model with credibility measure theory is constructed. Then the simulationbased tabu search algorithm combining inter-route and intra-route neighborhoods and embedded restarts are designed to solve it. Computational results show that the tabu search algorithm developed is superior to sweep algorithms and that compared with handling each on separate routes, the transportation costs can be reduced by 43% through combining pickups with deliveries.

Research on the fully fuzzy time-cost trade-off based on genetic algorithms

It is very difficult to estimate exact values of time and cost of an activity in project scheduling process because many uncertain factors, such as weather, productivity level, human factors etc. , dynamically affect them during project implementation process. A GAs-based fully fuzzy optimal time-cost trade-off model is presented based on fuzzy sets and genetic algorithms (GAs). In tihs model all parameters and variables are characteristics by fuzzy numbers. And then GAs is adopted to search for the optimal solution to this model. The method solves the time-cost trade-off problems under an uncertain environment and is proved practicable through a giving example in ship building scheduling.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation

In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.

Agents and Lattice-valued Logic

In fuzzy set theory, instead of the underlying membership set being a two-valued set it is a multi-valued set that generally has the structure of a lattice L with a minimal element O and the maximal element I. Furthermore if ∧, ∨, → and ┐ are defined in the set L, then we can use these operations to define, as in the ordinary set theory, operations on fuzzy subsets. In this paper we give a model of the Lattice-Valued Logic with set of agents. Any agents know the logic value of a sentence p. The logic value is compatible with all of the accessible conceptual models or worlds of p inside the agent. Agent can be rational or irrational in the use of the logic operation. Every agent of n agents can have the same set of conceptual models for p and know the same logic for p in this case the agents form a consistent group of agents. When agents have different conceptual models for p, different subgroup of agents know different logic value for p. In this case the n agents are inconsistent in the expression of the logic value for p. The valuation structure of set of agents can be used as a semantic model for the Lattice-valued Logic and fuzzy logic.

Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.