This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he...This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.展开更多
Since in most practical cases the processing time of scheduling is not deterministic, flow shop scheduling model with fuzzy processing time is established. It is assumed that the processing times of jobs on the machin...Since in most practical cases the processing time of scheduling is not deterministic, flow shop scheduling model with fuzzy processing time is established. It is assumed that the processing times of jobs on the machines are described by triangular fuzzy sets. In order to find a sequence that minimizes the mean makespan and the spread of the makespan, Lee and Li fuzzy ranking method is adopted and modified to solve the problem. Particle swarm optimization (PSO) is a population-based stochastic approximation algorithm that has been applied to a wide range of problems, but there is little reported in respect of application to scheduling problems because of its unsuitability for them. In the paper, PSO is redefined and modified by introducing genetic operations such as crossover and mutation to update the particles, which is called GPSO and successfully employed to solve the formulated problem. A series of benchmarks with fuzzy processing time are used to verify GPSO. Extensive experiments show the feasibility and effectiveness of the proposed method.展开更多
In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fu...In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.展开更多
A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural...A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.展开更多
Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an...Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.展开更多
In this papr, we introduce the notion ofT-fuzzy M-subsemigroups of M-semigroups by using a t-norm T and obtain some interesting properties. Further we show that the direct product of a T-fuzzy M-subsemigroup of R and ...In this papr, we introduce the notion ofT-fuzzy M-subsemigroups of M-semigroups by using a t-norm T and obtain some interesting properties. Further we show that the direct product of a T-fuzzy M-subsemigroup of R and a fuzzy M-subsemigroup of S is a T-fuzzy M-subsemigroup of M, Moreover, we prove that T-fuzzy M-subsemigroup of M is exhibited as the direct product of T-fuzzy M-subsemigroups of R and S respectively.展开更多
Let S and T be semigroups. F(S) and F,(S) denote the sets of all fuzzy subsets and all fuzzy subsemigroups of 5, respectively. In this paper, we discuss the homomorphisms between F(S)(Fs(S)) and F(T)(Fs(T)). We introd...Let S and T be semigroups. F(S) and F,(S) denote the sets of all fuzzy subsets and all fuzzy subsemigroups of 5, respectively. In this paper, we discuss the homomorphisms between F(S)(Fs(S)) and F(T)(Fs(T)). We introduce the concept of fuzzy quotient subsemigroup and generalize the fundamental theorems of homomorphism of semigroups to fuzzy subsemigroups.展开更多
Intuitionistic fuzzy sets are generalized fuzzy sets which were first introduced by Atanassov in 1986. In this paper, we introduce the concept of intuitionistic fuzzy M-subsemigroups of an M-semigroup M with respect t...Intuitionistic fuzzy sets are generalized fuzzy sets which were first introduced by Atanassov in 1986. In this paper, we introduce the concept of intuitionistic fuzzy M-subsemigroups of an M-semigroup M with respect to an s-norm S and a t-norm T on in-tuitionistic fuzzy sets and study their properties. In particular, intuitionistic (S,T)-direct products of M-semigroups are considered and some recent results of fuzzy M-subsemigroups of M-semigroups obtained by Zhan and Tan^[21] are extended and generalized to intuitionistic (S, T)-fuzzy M-subsemigroups over M-semigroups.展开更多
This paper considers the economic production quantity (EPQ) problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expec...This paper considers the economic production quantity (EPQ) problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expected value criterion and chance constrained criterion, a fuzzy expected value model (EVM) and a chance constrained programming (CCP) model are constructed. Then fuzzy simulations are employed to estimate the expected value of fuzzy variable and c^-level minimal average cost. In order to solve the CCP model, a particle swarm optimization (PSO) algorithm based on the fuzzy simulation is designed. Finally, the effectiveness of PSO algorithm based on the fuzzy simulation is illustrated by a numerical example.展开更多
基金Project supported by Faculty of Technology,Department of Electrical Engineering,University of Batna,Algeria
文摘This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.
基金The National Natural Science Foundation of China ( No.60774078)Innovation Foundation of Shanghai University ,Scientific Research Special Fund of Shanghai Excellent Young Teachers , Chenguang Project ( No.2008CG48)Shanghai Leading Academic Discipline Project ( No.T0103)
文摘Since in most practical cases the processing time of scheduling is not deterministic, flow shop scheduling model with fuzzy processing time is established. It is assumed that the processing times of jobs on the machines are described by triangular fuzzy sets. In order to find a sequence that minimizes the mean makespan and the spread of the makespan, Lee and Li fuzzy ranking method is adopted and modified to solve the problem. Particle swarm optimization (PSO) is a population-based stochastic approximation algorithm that has been applied to a wide range of problems, but there is little reported in respect of application to scheduling problems because of its unsuitability for them. In the paper, PSO is redefined and modified by introducing genetic operations such as crossover and mutation to update the particles, which is called GPSO and successfully employed to solve the formulated problem. A series of benchmarks with fuzzy processing time are used to verify GPSO. Extensive experiments show the feasibility and effectiveness of the proposed method.
基金National Natural Science Foundation of China(No.61663021)Science and Technology Support Project of Gansu Province(No.1304GKCA023)Scientific Research Project in University of Gansu Province(No.2017A-025)
文摘In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.
基金Project(51075289) supported by the National Natural Science Foundation of ChinaProject(20122014) supported by the Doctor Foundation of Taiyuan University of Science and Technology,China
文摘A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.
基金National Natural Science Foundation of China(No.60873179)Doctoral Program Foundation of Institutions of Higher Education of China(No.20090121110032)+3 种基金Shenzhen Science and Technology Research Foundations,China(No.JC200903180630A,No.ZYB200907110169A)Key Project of Institutes Serving for the Economic Zone on the Western Coast of the Tai wan Strait,ChinaNatural Science Foundation of Xiamen,China(No.3502Z2093018)Projects of Education Depart ment of Fujian Province of China(No.JK2009017,No.JK2010031,No.JA10196)
文摘Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.
基金the Natural Foundation of Education Committee of Hubei Province (2004Z002, D200529001)
文摘In this papr, we introduce the notion ofT-fuzzy M-subsemigroups of M-semigroups by using a t-norm T and obtain some interesting properties. Further we show that the direct product of a T-fuzzy M-subsemigroup of R and a fuzzy M-subsemigroup of S is a T-fuzzy M-subsemigroup of M, Moreover, we prove that T-fuzzy M-subsemigroup of M is exhibited as the direct product of T-fuzzy M-subsemigroups of R and S respectively.
基金Supported by NNSF of China(19971028)and Natural Science Foundations [(011438)(021073),(Z02017)] of Guangdong Province.
文摘Let S and T be semigroups. F(S) and F,(S) denote the sets of all fuzzy subsets and all fuzzy subsemigroups of 5, respectively. In this paper, we discuss the homomorphisms between F(S)(Fs(S)) and F(T)(Fs(T)). We introduce the concept of fuzzy quotient subsemigroup and generalize the fundamental theorems of homomorphism of semigroups to fuzzy subsemigroups.
基金the National Natural Science Foundation of China(60474022)the Key Science Foundation of Education Committee of Hubei Province(2004Z002D200529001).
文摘Intuitionistic fuzzy sets are generalized fuzzy sets which were first introduced by Atanassov in 1986. In this paper, we introduce the concept of intuitionistic fuzzy M-subsemigroups of an M-semigroup M with respect to an s-norm S and a t-norm T on in-tuitionistic fuzzy sets and study their properties. In particular, intuitionistic (S,T)-direct products of M-semigroups are considered and some recent results of fuzzy M-subsemigroups of M-semigroups obtained by Zhan and Tan^[21] are extended and generalized to intuitionistic (S, T)-fuzzy M-subsemigroups over M-semigroups.
基金supported by the National Natural Science Foundation of China under Grant No. 70471049
文摘This paper considers the economic production quantity (EPQ) problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expected value criterion and chance constrained criterion, a fuzzy expected value model (EVM) and a chance constrained programming (CCP) model are constructed. Then fuzzy simulations are employed to estimate the expected value of fuzzy variable and c^-level minimal average cost. In order to solve the CCP model, a particle swarm optimization (PSO) algorithm based on the fuzzy simulation is designed. Finally, the effectiveness of PSO algorithm based on the fuzzy simulation is illustrated by a numerical example.