The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is...The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.展开更多
In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight ...In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.展开更多
The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing s...The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.展开更多
The study of pedestrian evacuation in channels with different structures is important among optimizing through efficiency,avoiding accidents and designing passageway.It is of significant reference to set up a specific...The study of pedestrian evacuation in channels with different structures is important among optimizing through efficiency,avoiding accidents and designing passageway.It is of significant reference to set up a specific model on account of the real traffic system and design an appropriate controller to apply on it.This paper establishes a macroscopic model for T-shaped channel with a number of controlled entrances basing on the law of mass conservation.Then,with the method of cascade,a kind of sliding mode controller is designed to achieve the control target of avoiding the blocking and maximizing the pedestrian flow of the whole model,the stability of the system is proved by the Lyapunov stability theory,and the boundary layer method is applied to restrain the chattering of the controller.Finally,simulation results show the efficiency of the sliding mode controller and the improvement brought by the boundary layer method.展开更多
基金supported by the National Natural Science Foundation of China(6137415361473138)+2 种基金Natural Science Foundation of Jiangsu Province(BK20151130)Six Talent Peaks Project in Jiangsu Province(2015-DZXX-011)China Scholarship Council Fund(201606845005)
文摘The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.
基金The National Natural Science Foundation of China(No.61374153,61473138,61374133)the Natural Science Foundation of Jiangsu Province(No.BK20151130)+1 种基金Six Talent Peaks Project in Jiangsu Province(No.2015-DZXX-011)China Scholarship Council Fund(No.201606845005)
文摘In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.
基金supported by the National Natural Science Foundation of China(11574120,U1636117)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing,Ministry of Education,China(UASP1503)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20161359)Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project
文摘The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.
基金the National Natural Science Founda-tion of China(No.61374133)the Natural Science Foun-dation of Jiangsu Province(No.BK20191286)the Fundamental Research Funds for the Central Universi-ties(No.30920021139)。
文摘The study of pedestrian evacuation in channels with different structures is important among optimizing through efficiency,avoiding accidents and designing passageway.It is of significant reference to set up a specific model on account of the real traffic system and design an appropriate controller to apply on it.This paper establishes a macroscopic model for T-shaped channel with a number of controlled entrances basing on the law of mass conservation.Then,with the method of cascade,a kind of sliding mode controller is designed to achieve the control target of avoiding the blocking and maximizing the pedestrian flow of the whole model,the stability of the system is proved by the Lyapunov stability theory,and the boundary layer method is applied to restrain the chattering of the controller.Finally,simulation results show the efficiency of the sliding mode controller and the improvement brought by the boundary layer method.
