共查询到18条相似文献,搜索用时 859 毫秒
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针对模糊自整定控制器参数寻优能力差的不足,研究了采用自适应交叉概率与变异概率的遗传算法,提出了用这种自适应遗传算法改善模糊自整定控制器性能的方法。对采用自适应遗传算法的模糊自整定控制器与一般的模糊自适应控制器作了仿真对比研究,说明了前者的优越性。 相似文献
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基于改进遗传算法的半主动悬架系统模糊控制优化研究 总被引:2,自引:0,他引:2
该文利用ADAMS 软件建立了汽车半主动悬架系统多体模型,通过ADAMS/Control 模块将悬架模型从ADAMS/View中导入MATLAB/Simulink 环境中,然后,完成模糊控制器的设计。模糊控制器的模糊规则由Matlab语言编写的改进遗传算法进行优化,实现汽车半主动悬架系统多体模型模糊控制器的改进遗传算法优化设计。为了检验模糊控制器的控制效果和改进遗传算法的优化性能,在C级路面下,以25m/s和35m/s两种不同车速对半主动悬架系统和被动悬架系统进行对比分析。仿真结果表明:基于改进遗传算法的半主动悬架系统模糊控制能够显著改善汽车的行驶平顺性。 相似文献
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针对船舶横摇的非线性模型,利用免疫反馈机理,设计了一种减摇鳍模糊免疫自适应PID控制器。控制器将模糊控制与PID控制相结合,采用Fuzzy推理,对非线性函数进行模糊逼近,用模糊免疫P调节器实时整定PID控制器的比例增益,采用常规模糊控制器在线调整免疫PID控制器的积分时间常数和微分时间常数。通过对船舶减摇鳍控制系统的仿真,可以看出采用模糊免疫自适应PID控制器其控制效果远优于常规PID控制器,使减摇鳍的减摇效果得到显著提高。 相似文献
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基于改进遗传算法的胶印质量控制方法研究 总被引:1,自引:1,他引:0
针对前馈学习算法容易陷入局部最优以及收敛速度慢等缺点,采用遗传算法来优化模糊神经网络中的参数,通过仿真实验证明,该控制器算法具有较快的收敛速度和较强的局部搜索能力. 相似文献
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针对遗传算法中交叉概率和变异概率的缺陷,设计了一种基于模糊逻辑控制器的自适应遗传算法,实验结果表明,该算法具有较好的在线性能。 相似文献
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ALV路径跟踪模糊控制及参数GA寻优 总被引:1,自引:0,他引:1
本文分析和设计了一种适合于ALV(Autonomous Land Vehice地面自主车)路径跟踪的模糊控制器,并采用遗传算法(Genetic Algorithms,简称CA)优化模糊规则及隶属函数的大量参数,从仿真研究的结果来看,提出的方法是有效的。 相似文献
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An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently. 相似文献
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Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system. 相似文献
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In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm. 相似文献
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This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model. 相似文献
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Javad KhodaeiMehr Samaneh Tangestanizadeh Ramin Vatankhah Mojtaba Sharifi 《IET systems biology》2018,12(4):154
Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro‐fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro‐fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi–Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute‐infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.Inspec keywords: epidemics, diseases, blood, medical computing, microorganisms, genetic algorithms, fuzzy control, neurocontrollers, adaptive control, medical control systemsOther keywords: genetic algorithm, hepatitis C blood born virus, liver disease, hepatitis C virus infection, epidemiology, intelligent controller, optimal adaptive neuro‐fuzzy controller, adaptive neuro‐fuzzy inference system, ANFIS, genetic algorithm, control signal, state condition, objective function minimisation, Takagi‐Sugeno fuzzy structure, acute‐infected individuals, ordinary optimal control methods, HCV epidemic 相似文献