The paper presents a constrained optimization procedure to design a DC-DC converter with coupled inductors for minimizing the power losses. Two algorithms have been used, Genetic and particleswarmoptimization algori...
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ISBN:
(纸本)9781467388634
The paper presents a constrained optimization procedure to design a DC-DC converter with coupled inductors for minimizing the power losses. Two algorithms have been used, Genetic and particle swarm optimization algorithm, and the results have been compared. In particular, with the proposed technique, the electrical, magnetic and geometrical characteristics of the coupled inductors have been obtained and the values of duty cycle and frequency have been defined in order to obtain the maximum efficiency.
Designing DNA sequence sets is a fundamental issue in the fields of nanotechnology and nanocomputing. Not only quality but also quantity of DNA coding sequences affect the reliability of DNA computing. For this reason...
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Designing DNA sequence sets is a fundamental issue in the fields of nanotechnology and nanocomputing. Not only quality but also quantity of DNA coding sequences affect the reliability of DNA computing. For this reason, many researchers have paid their attentions to find more and better DNA sequences in DNA computing. In this paper we present particle swarm optimization algorithm (PSO) for the design of DNA sequence sets, namely sets of equal-length sequence over the nucleotides alphabet A,C,G,T that satisfy H-distance constraint. In our computational experiments, we succeed in generating better sequences sets. We give some practical values which satisfy H-distance constraint, and it has some directions for the theoretical lower bound in DNA computing.
Using particleswarmoptimization (PSO) has become a common heuristic technique in many fields of engineering. In this paper, we apply PSO to solve Joint Multiuser and Inter-symbol Interference (ISI) Suppression probl...
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ISBN:
(纸本)9781467356343
Using particleswarmoptimization (PSO) has become a common heuristic technique in many fields of engineering. In this paper, we apply PSO to solve Joint Multiuser and Inter-symbol Interference (ISI) Suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel, to reduce the computational complexity. In the proposed method, conventional detector (CD) is used as the first stage to initialize the PSO algorithm and time-varying acceleration coefficients (TVAC) are used in PSO algorithm. The simulation results show that the performance of PSO-based MUD with TVAC is promising and outperform the CD.
A multi-objective, multi-level, multi-product, multi-constraint batch planning problem is abstracted from production of diaphragm caustic soda, and the problem is formulated as a mathematic optimization model with con...
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ISBN:
(纸本)9781424427239
A multi-objective, multi-level, multi-product, multi-constraint batch planning problem is abstracted from production of diaphragm caustic soda, and the problem is formulated as a mathematic optimization model with constraints involved resources, work manufacture processes and production capacity etc. Some sub_objectives were considered in the problem model, such as total profit amount, profit margin, total energy wastage and wastage per ten thousand RMB.A modified particle swarm optimization algorithm with a percent-coding and dynamic-bounds coding scheme is proposed for the problem. The validity and flexibility of the model and algorithm are verified by calculating the data from production practices numerically.
Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity. Transportation costs, distribution center construction costs, ord...
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Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity. Transportation costs, distribution center construction costs, ordering costs, safe inventory costs and inventory holding costs are the important parts of the total logistics costs. In this paper, based on the research results of LMRP( location model of risk pooling) location with fixed construction cost, the LMRPVCC ( location model of risk pooling based on variable construction cost) will be introduced. Applying particleswarmoptimization to several computational instances, the authors find the suboptimum solution of the model.
The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic *** drawbacks such as complicated genetic operation, tardy ...
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The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic *** drawbacks such as complicated genetic operation, tardy convergent speed and the aptness to sink into local minimum within the Genetic algorithm(GA) have encouraged a satellite broadcasting scheduling approach for resolving the scheduling *** approach was based on the particleswarmoptimization(PSO) algorithm which involved in processes such as constructing the model of satellite broadcasting scheduling,initialization of the particles and particle *** has been shown by simulation analysis that satellite broadcasting scheduling based on the PSO algorithm was feasible and its optimization result was significant.
This paper proposes an improved neural network adaptive sliding mode control method based on the neural network sliding mode control to improve the performance of the robot trajectory tracking control. This new scheme...
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This paper proposes an improved neural network adaptive sliding mode control method based on the neural network sliding mode control to improve the performance of the robot trajectory tracking control. This new scheme regards neural network as a controller and adopts robust control law to eliminate the approximation error which uses its nonlinear mapping ability to approximate various unknown nonlinear systems. Hidden layer units and network structure parameter have an effect on neural network mapping. So we will decrease chattering. We conduct experiments with three joints robot and give an example to verify this paper's scheme under the MATLAB platform. The results of experiments show that the new neural network adaptive sliding mode control has a good control accuracy and robustness than other control methods. The new scheme reduces the chattering effectively and also decreases the effect of hidden layer units and network structure parameter on neural network mapping. It will be a good choice for the robot trajectory tracking control.
This paper presents a higher-order multivari-ate Markov chain model combined with particleswarmoptimization *** to some deciencies,such as only considering the maximum probability while ignoring the effect of the ot...
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This paper presents a higher-order multivari-ate Markov chain model combined with particleswarmoptimization *** to some deciencies,such as only considering the maximum probability while ignoring the effect of the other probabilities,the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefcient of level characteristics *** recent years,air pollution acutely aggravates chronic diseases in mankind,such as sulfur dioxide pollution which plays a most important role in acid *** order to confront air pollution problems and to plan abatement strategies,both the scientic community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants *** the forecast of air pollutants as a case,we illustrate the improvement of accuracy and efciency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.
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 o...
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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 accuracy. In 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 SVR. Additionally, the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, 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.
Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for s...
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Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results: This paper presents an effective search strategy for a particleswarmoptimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particleswarmoptimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study.
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