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Numerical Investigation on the Transmission Loss of Skin Panels Based on the Intelligent pso-CGA algorithm

IEEE ACCESS 2018年 6卷 26121-26130页

作者： Zhang, Hui Xu, Guo-Liang Yin, Wei Wang, Hong-Bo Ge, Peng Beijing Inst Struct & Environm Engn Beijing 100076 Peoples R China

In the reported research, they only analyzed the impacts of restraint damping parameters on acoustic characteristics of skin panels, but failed to adopt advanced optimization algorithms to conduct an optimization design on restraint damping effects. In addition, they did not conduct further research on noises in cabins with skin panels as the basis. Aiming at these questions, according to the acoustic model and the pso-CGA algorithm, the acoustic characteristics of the skin panel were conducted a multi-objective optimization, and a better structure with low noise and light weight was finally obtained. The computational model was also validated by its experimental test. In order to verify the effectiveness of the pso-CGA algorithm after parameter selection, it was compared with the traditional CGA and pso algorithm. Three kinds of algorithms adopted the same population to conduct a multi-objective optimization of transmission loss of skin panels. Optimized results showed that: the pso-CGA model in the paper had a high clustering degree. Transmission loss of most iteration points was obviously more than those of other two kinds of algorithms. Noises in the cabin could be improved effectively.

关键词： Skin panels transmission loss CGA algorithm pso algorithm novel pso-CGA algorithm

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Heterogeneous Acceleration of Hybrid pso-QN algorithm for Neural Network Training

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IEEE ACCESS 2019年 7卷 161499-161509页

作者： Yan, Shun Liu, Qiang Li, Jiajun Han, Liang Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Alibaba Grp Sunnyvale CA 94085 USA

Artificial neural network (ANN) has successfully provided solutions to many practical problems. One of the difficulties in training ANNs is finding the ideal solution to the network weights quickly. This paper designs an implementation of the hybrid particle swarm optimization (pso) and quasi-Newton (QN) algorithm on CPU-GPU platform using OpenCL to accelerate ANN training. The pso-QN implementation combines the strength of the pso algorithm in global search and the advantage of the QN algorithm in fast convergence rate. A configurable parallel line search implementation and an efficient parallel reinitialization implementation are proposed to improve the performance and reduce data transmission. Experiments show the pso-QN hybrid parallel implementation on CPU-GPU platform can achieve up to $362\times$ and $8.9\times$ acceleration compared with the C implementations of pso and BFGS-QN on CPU, respectively. Compared with the pso and BFGS-QN parallel implementations, the training loss of the pso-QN hybrid implementation at given training time is reduced by 15.16 and 3.97, and the testing error is reduced by 13.66 and 6.86, respectively.

关键词： Training Convergence Acceleration Neural networks Optimization Neurons Approximation algorithms Heterogeneous computing neural network training pso algorithm quasi-Newton algorithm

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PID Control for the Vehicle Suspension Optimized by the pso algorithm

PID Control for the Vehicle Suspension Optimized by the PSO ...

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第三届机电控制技术与交通运输国际学术会议

作者： Yongdong Xie Jie Meng Suzhou Institute of Construction & Communications Jiangsu Union Technical Institute School of automotive Engineering Changshu Institute of Technology

To solve the problems of the PID controller when it is used for the vehicle suspension, a method using the pso algorithm is designed. This method utilizes the global searching strategy of the pso algorithm to design and optimize the parameters of the target function for the suspension performance indexes matrix. And then a simulation experiment is provided. The simulation results show that the performances of the actively controlled vehicle suspension using the PID controller optimized by the pso algorithm can be greatly improved compared to the suspension controlled by the normal PID controller and the passive one. It means that the problems of defining the weight matrices are well solved and the advantage of the normal PID controller is utilized sufficiently.

关键词： Suspension pso algorithm PID controller Automotive control performance

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pso-algorithm-Based Optimal Design of LCLC Resonant Converters for Space Travelling-Wave Tube Amplifiers Applications

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ENERGIES 2019年第12期12卷 2444-2444页

作者： Zhao, Bin Wang, Gang Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China

Due to the advantages of zero-voltage-switching (ZVS) and zero-current-switching (ZCS), LCLC resonant converters are universally applied in two-stage electronic power conditioners (EPCs) of space travelling-wave tube amplifiers (TWTAs). In two-stage EPCs, as the output voltage is regulated by the first stage, the main functions of LCLC resonant converters are to boost the input voltage, provide galvanic isolation, and maintain high efficiency. However, the total power loss of an LCLC resonant converter is very challenging because of the multiple resonant components and their mutual couplings. In order to solve this problem, in this paper, a pso (Particle Swarm Optimization)-algorithm-based, efficiency-oriented optimal design method of an LCLC resonant converter is proposed. Based on an analysis of working principles, the total power loss (with consideration of all the power losses, including the driving loss and the conduction loss of the main switches), transformer loss, and power loss of the rectifiers is calculated. After that, the total power loss is appointed as the objective function of the pso algorithm, where the optimal circuit parameters are derived to minimize the total power loss. After the optimal circuit parameters are obtained, an LCLC resonant converter with the desired parameters is built. Finally, the proposed pso-algorithm-based, efficiency-oriented optimal design method is validated by an example.

关键词： space travelling-wave tube Amplifiers LCLC resonant converter pso algorithm optimal design method total power loss

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Learning Interval-Valued Fuzzy Cognitive Maps with pso algorithm for Abnormal Stock Return Prediction 1

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6th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Hajek, Petr Prochazka, Ondrej Univ Pardubice Fac Econ & Adm Inst Syst Engn & Informat Studentska 84 Pardubice 53210 Czech Republic

ISBN: (数字)9783319710693

ISBN: (纸本)9783319710693;9783319710686

Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with pso algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with pso learning.

关键词： Stock market Interval-valued fuzzy cognitive map pso algorithm Abnormal stock return

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A pso algorithm Based Two-degree-of-freedom IMC Scheme for Large Time-delay Process

A PSO Algorithm Based Two-degree-of-freedom IMC Scheme for L...

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Chinese Automation Congress (CAC)

作者： Tang, Wei Yuan, Zhimin Dang, Shihong Shan, Wenjuan Shaanxi Univ Sci & Technol Coll Elect & Informat Engn Xian 710021 Shaanxi Peoples R China Shaanxi Univ Sci & Technol Coll Light Ind Sci & Engn Xian 710021 Shaanxi Peoples R China

ISBN: (纸本)9781538635247

With the aid of double-controller design concept and particle swarm optimization (pso) algorithm, an internal model control (IMC) scheme with two-degree-of-freedom (TDFIMC) based on pso algorithm (pso-TDF-IMC) is proposed, which is capable of controlling large time-delay processes. This method takes integral time absolute error (ITAE) index as objective function and uses pso algorithm to optimize IMC filter time constants. Simulation results show that the presented pso-TDF-IMC scheme has such advantages as simplicity for algorithm, fast searching speed and high efficiency, which can greatly improve the performance and robustness of TDF-IMC controller.

关键词： large time-delay process TDF-IMC the filter time constant pso algorithm

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A pso algorithm applied to a PID controller for motion mobile robot in a complex dynamic environment

A PSO algorithm applied to a PID controller for motion mobil...

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International Conference on Engineering and MIS (ICEMIS)

作者： Aouf, Awatef Boussaid, Lotfi Sakly, Anis Natl Engn Sch Sousse Dept Elect Engn Sousse Tunisia Natl Engn Sch Monastir Dept Elect Engn Monastir Tunisia

ISBN: (纸本)9781509067787

In robot navigation problem, many variables should be controlled in order to obtain the best upshot at the end of task. Indeed, the mobile robot ought to avoid obstacles and reach its final destination with the nethermost time and the shortest trajectory. It should be stable, accurate and replies quickly. Motivated by these demands, Particle Swarm Optimization for optimal PID controller for motion planning in a real complex environment is developed. Comparing to a Fuzzy logic controller, simulation results shows the efficiency of our suggested method.

关键词： Mobile robot differential kinematics pso algorithm fuzzy logic control navigation

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A Novel Strategy for Adjusting Current Pulse Amplitude of FES-Systems with PID based on pso algorithm Method to Control the Muscle Force 14

A Novel Strategy for Adjusting Current Pulse Amplitude of FE...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Ben Hmed, Abdennacer Bakir, Toufik Garnier, Yoann Binczak, Stephane Sakly, Anis Univ Bourgogne Franche Comte Arts & Metiers CNRS Lab Le2i UMR 6306 Dijon France Univ Monastir Natl Sch Engineers Monastir Res Unit ESIER Monastir Tunisia Univ Bourgogne Franche Comte Lab INSERM UMR 1093 CAPS Dijon France

ISBN: (纸本)9789897583049

Adjusting stimulation parameters using control strategy based on mathematical model, that successfully predict muscle force, may improve the efficiency of Functional Electrical Stimulation (FES) systems. It present an interesting task in industrial FES systems applications. In the present study, we investigate the PID control tuning based on the Particle Swarm Optimization (pso) algorithm at the first time in neuro-muscular systems for updating automatically the stimulation pulse amplitude to track a desired force profiles. In the beginning, The pso algorithm is used to identify unknown force model parameters. Next, according to the identified model, optimal PID gains are found by the same intelligent algorithm. The preliminary obtained results showed promise of using intelligent algorithm on tuning PID to perform control sessions of FES systems.

关键词： Functional Electrical Stimulation(FES) Muscle Force Model PID Controller pso algorithm Pulse Amplitude

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Multiple Models Adaptive Predictive Control based on pso algorithm 16

Multiple Models Adaptive Predictive Control based on PSO Alg...

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16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Liu Gui-ying Qu Li-ping Li Yun-feng Beihua Univ Elect Informat Engn Coll Jilin 132021 Jilin Peoples R China JiLin 524 Factory Jilin 132021 Jilin Peoples R China

ISBN: (纸本)9781538621622

In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (pso) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by pso algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.

关键词： pso algorithm Multiple models Predictive control Time lag

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A pso algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年第0期98卷 53-62页

作者： Yuguang, Zhong Bo, Ai Yong, Zhan Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

Hull assembly line (HAL) is a mixed-model assembly line on which different hull blocks can be assembled at the same time. Aiming at the balance problem of HAL, minimizing the cycle time, minimizing the static load *** workstations, minimizing the dynamic load balancing in all workstations, and minimizing the multi-station associated complexity are considered as optimization objectives. An improved discrete particle swarm optimization (IDpso) algorithm based on the stratified optimization idea is developed for scheduling of the multi-objective problem. In the proposed algorithm, particles were coded by a two-dimensional task-oriented representation method, and then collaboration and competition of particle individuals are simulated by crossover and mutation operators in the genetic algorithm (GA). The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Finally, a practical case is used to analyze the effectiveness and feasibility of the stratified scheduling strategy. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Assembly line balancing problem Shipbuilding industry pso algorithm Stratified optimization

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