A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermomet...
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A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermometer is effectively *** dynamic compensator is established and the compensation is described and explicated by the Wiener *** to Wiener model,the novel structure is *** identification of thermometer non-linear dynamic compensator is achieved by particleswarmoptimization *** results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.
Keeping particleswarm alive support vector machine optimized algorithm network traffic forecasting model(EPSO-SVM) is proposed. First, building support vector machine learning sample by calculating the delay time and...
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Keeping particleswarm alive support vector machine optimized algorithm network traffic forecasting model(EPSO-SVM) is proposed. First, building support vector machine learning sample by calculating the delay time and embedding dimension, second, learning network flow training set by using the maintaining the vitality of particleswarmoptimization support vector machine, at last, validating performance EPSO-SVM's by using set of the network traffic tests. The results showed that the proposed model can improve the forecasting precision of network traffic. It has great practical application value.
particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we at...
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particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we attempt to introduce parallel mechanism into PSO and proposes PPSO(Parallel PSO) *** test the PPSO on four widely known benchmark functions and the experiment results show the efficiency and efficacy of PPSO.
The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost...
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The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost by determining the location of the warehouse,allocating appropriate nurnber of vehicles for the selected warehouse and finding the optimal routing for each *** the same time,Improved particle swarm optimization algorithm with Genetic algorithm and Simulated Annealing were applied to solve an example of the *** good convergence state of the global best solution has proved the IVRP model was correct and the algorithm applied to solve IVRP was effective.
Threshold extraction is the fundamental step in multi-threshold image *** paper has introduced particle swarm optimization algorithm (PSO) for threshold *** when dealing with the peaky high dimension function of maxim...
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Threshold extraction is the fundamental step in multi-threshold image *** paper has introduced particle swarm optimization algorithm (PSO) for threshold *** when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called *** can cause image segmentation *** paper proposes a modified particleswarmoptimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold *** results show that the MPSO has better performance and quicker *** experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.
A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilev...
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A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified particleswarmoptimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods.
In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR),irrelevant or correlated features in the samples could spoil the performance of the SVR classifier,leading to decrease of ...
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In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR),irrelevant or correlated features in the samples could spoil the performance of the SVR classifier,leading to decrease of prediction *** order to solve the problems mentioned above,this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of ***,the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit ***,we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR).The experiment indicates that the proposed method is quite effective and ubiquitous.
This paper introduces two instances of deceptive problems of particleswarmoptimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** add...
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This paper introduces two instances of deceptive problems of particleswarmoptimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** addition,we empirically verified the correctness of our theoretical *** order to solve the two deceptive problems,we propose an algorithm ISA-PSO (inverse search area-PSO),which can change the particles' search direction and expand their search area at the appropriate *** experimental results show the effectiveness of our algorithm ISA-PSO.
This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and min...
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This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the *** a given travel demand and specified capacity of stops,the model is solved by heuristic *** improved discrete particleswarmoptimization(PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and *** the algorithm,a stop based representation is designed,and a new method is used to update the position and velocity of *** order to keep the particleswarmalgorithm from premature stagnation,the simulated annealing algorithm,which has local search ability,is combined with the PSO algorithm to make elaborate search near the optimal solution,then the quality of solutions is improved *** empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the *** experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm,and an optimal set of stopping schemes can always be generated for a given *** achieve the best planning outcome,the stopping schemes should be flexibly planned,and not constrained by specific ones as often set by the planner.
The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimizationalgorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas...
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The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimizationalgorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological-evolution-based optimizationalgorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm-intelligence-based method-particleswarmoptimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki-Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.
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