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ZEBRA battery SOC estimation using pso-optimized hybrid neural model considering aging effect

IEICE ELECTRONICS EXPRESS 2012年第13期9卷 1115-1121页

作者： Gharavian, Davood Pardis, Reza Sheikhan, Mansour Shahid Abbaspour Univ Technol EE Dept Tehran Iran Islamic Azad Univ S Tehran Branch EE Dept Tehran Iran

The state of charge (SOC) estimation for electric vehicles (EVs) is important and helps to optimize the utilization of the battery energy storage in EVs. In this way, aging is also a key parameter impacting the performance of batteries. In this paper, a hybrid neural model is proposed for the SOC estimation of ZEBRA (Zero Emission Battery Research Activities) battery considering the aging effect through the state of health (SOH) and the discharge efficiency (DE) parameters. The number of hidden nodes in neural modules is also optimized using particle swarm optimization (pso) algorithm. The SOC estimation error of the proposed system is 1.7% when compared with the real SOC obtained from a discharge test.

关键词： hybrid neural networks state of charge estimation pso algorithm

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A pso-based semi-analytical force prediction model for chamfered carbide tools considering different material flow state caused by edge geometry

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2020年第0期169卷 105329-000页

作者： Weng, Jian Zhuang, Kejia Hu, Cheng Ding, Han Wuhan Univ Technol Sch Mech & Elect Engn Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The prediction of force in metal cutting is essential when estimating tool wear, surface quality and energy consumption. As a significant characteristic in tool design, edge geometry results in more challenges in force modeling especially under the condition of cutting with chamfered insert. In this paper, a semi-analytical model is proposed to describe the effect of chamfered edge on cutting force considering multiple material flow state. In orthogonal cutting, shear effect is calculated based on the unequal division shear-zone model and Johnson-Cook's material constitutive law. Two kinds of chip-tool contact patterns are considered to discuss the effect of tool edge, chip flows on both chamfered edge and rake face when chamfer length is close to Uncut Chip Thickness (UCT), or chip flows only on chamfered edge when chamfer length is much larger than UCT. Edge force coefficients of chamfered edge are modeled as linear functions with respect to shear stress, chamfer length and chamfer angle. The variation of edge force with respect to edge geometry is studied through Finite Element Method (FEM) simulations. The modified force prediction model is established by introducing a factor S to describe the trend of edge effect with respect to the ratio of chamfer length to UCT. Finally, a modified force prediction model is presented and the constants in the model can be calibrated through a Particle Swarm Optimization (pso)-based algorithm. Experiments of orthogonal cutting AISI304 with different chamfered carbide inserts are performed to validate the correctness and feasibility of the proposed model. The experimental results show that this model is effective in predicting the cutting force components under the condition of different edge preparations and cutting parameters.

关键词： Semi-analytical mode Cutting force Chamfered tool pso algorithm

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Steel Moment-Resisting Frame Reliability via the Interval Analysis by FCM-pso Approach considering Various Uncertainties

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JOURNAL OF EARTHQUAKE ENGINEERING 2020年第1期24卷 109-128页

作者： Jough, Fooad Karimi Ghaleh Sensoy, Serhan Islamic Azad Univ Dept Civil Engn Sarab Branch Sarab Iran Eastern Mediterranean Univ Fac Engn Dept Civil Engn Famagusta North Cyprus Turkey

One important issue in compiling siesmic fragility curves is blending and incorporating uncertainties into the model under seismic conditions. Methods used in this regard are either approximate, costly and time-consuming. To address this, the optimized fuzzy method is used. To compile the fragility curve, epistemic and aleatory uncertainties have been incorporated in which model parameters are fuzzy values and FCM-pso method is used to estimate mean and standard deviation of fragility curve. The FCM-pso algorithm is trained using scenarios compiled via the IDA method. Results obtained from the full Monte Carlo method were used for verification. The method employed here is advantageous with regard to both accuracy and execution time.

关键词： pso algorithm Fuzzy Method Collapse Fragility Curve Epistemic Uncertainty Interval Analysis

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Hyperchaos synchronization using pso-optimized RBF-based controllers to improve security of communication systems

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NEURAL COMPUTING & APPLICATIONS 2013年第5期22卷 835-846页

作者： Sheikhan, Mansour Shahnazi, Reza Garoucy, Sahar Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran Islamic Azad Univ Modeling & Optimizat Res Ctr Sci & Engn South Tehran Branch Tehran Iran

This paper studies the Lorenz hyperchaos synchronization and its application to improve the security of communication systems. Two methods are proposed to synchronize the general forms of hyperchaotic systems, and their performance in secure communication application is verified. These methods use the radial basis function (RBF)-based neural controllers for this purpose. The first method uses a standard RBF neural controller. Particle swarm optimization (pso) algorithm is used to derive and optimize the parameters of the RBF controller. In the second method, with the aim of increasing the robustness of the RBF controller, an error integral term is added to the equations of RBF neural network. For this method, the coefficients of the error integral component and the parameters of RBF neural network are also derived and optimized via pso algorithm. For better comparison, the proposed methods and an optimal PID controller optimized by pso are applied to the Lorenz hyperchaotic system for secure communication. Simulation results show the effectiveness and superiority of the proposed methods in both performance and robustness in comparison with the PID controller.

关键词： Hyperchaotic synchronization RBF pso algorithm Secure communication

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A Customized pso Model for Large-Scale Many-Objective Software Package Restructuring Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 10147-10162页

作者： Prajapati, Amarjeet JIIT Comp Engn & IT Dept Noida India

Recently, a variety of large-scale many-objective optimization algorithms (LSMaOAs) have been designed and proposed to address different classes of large-scale many-objective optimization problems (LSMaOPs). Even after tremendous progress in the development of LSMaOAs for the various types of synthetic LSMaOPs, the real-world LSMaOPs such as large-scale many-objective software package restructuring (LSMaOSPR) gained little attention. This work proposes a particle swarm optimization (pso) based LSMaOA for the LSMaOSPR problem. To this contribution, different components of pso framework such as selection of inertia weight, selection of cognitive and social constant, updating velocity and position of particles, and determination of personal best and global best are customized based on the suitability of the LSMaOSPR characteristics. To evaluate the supremacy of the proposed approach, we tested it over five LSMaOSPR problems. The optimization results indicate that the proposed LSMaOA approach has enough capability for generating an evenly distributed and well-converged approximation of the Pareto front for the large and complex LSMaOSPR problems.

关键词： Software package restructuring Many-objective optimization Large-scale optimization pso algorithm

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Tool life prediction of dicing saw based on pso-BP neural network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第11-12期123卷 4399-4412页

作者： Shi, Jun Zhang, Yanyan Sun, Yahui Cao, Weifeng Zhou, Lintao Zhengzhou Univ Light Ind Zhengzhou 450000 Henan Peoples R China Zhengzhou GLRH Technol Co Ltd Zhengzhou 450001 Henan Peoples R China

The quality of the dicing will be impacted if the tool wears out quickly during the dicing operation. If the crew changes the tool in a timely manner, the workpieces' quality of dicing is guaranteed. Therefore, for actual production, estimating the tool's remaining usable life (RUL) is crucial. A model was proposed in the research to predict the RUL of dicing tool. The back-propagation neural network (BP) and the particle swarm optimization (pso) algorithm are combined in the model. The model is also known as the pso-BP prediction model, where the inertia weight of the pso method can be changed in a more real-time and dynamic way. After several experiments, comparing the experimental results of the proposed model with two traditional models, it was found that the accuracy of the pso prediction model improved by 0.664% over the BP prediction model and by 0.661% over the traditional pso-BP (also called as Tpso-BP) prediction model. This concludes that the proposed prediction model is used to predict the RUL of the tool;the results will be more accurate, so the staff can replace the tool in time to ensure the quality and productivity.

关键词： BP neural network pso algorithm Data-driven model Tool life Dicing saw

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DESIGN OF pso FUZZY NEURAL NETWORK CONTROL FOR BALL AND PLATE SYSTEM

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第12期7卷 7091-7103页

作者： Dong, Xiucheng Zhao, Yunyuan Xu, Yunyun Zhang, Zhang Shi, Peng Xihua Univ Prov Key Lab Signal & Informat Proc Chengdu 610039 Peoples R China Univ Glamorgan Dept Comp & Math Sci Pontypridd CF37 1DL M Glam Wales Victoria Univ Sch Engn & Math Melbourne Vic 8001 Australia

The ball and plate system is a typical multi-variable plant, which is the extension of the traditional ball and beam problems. Particle swarm optimization algorithm fuzzy neural network control (pso-FNNC) scheme is introduced for the ball and plate system. The fuzzy neural network (FNNC) is optimized by the offline particle swarm optimization (pso) of global searching ability, and the online radius basis function (RBF) algorithm ability of local searching. Then, the optimized fuzzy RBF neural network (FRBF) tuned PID controller. The simulation results demonstrate the potential of the proposed technique, especially tracking speed, tracking accuracy and robustness, is improved obviously, which embodies the nice characters of the pso-FNNC scheme.

关键词： Ball and plate Fuzzy neural network pso algorithm PID

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Procreant pso for fastening the convergence to optimal solution in the application of document clustering

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CURRENT SCIENCE 2009年第1期96卷 137-143页

作者： Premalatha, K. Natarajan, A. M. Kongu Engn Coll Erode 638052 India Bannari Amman Inst Technol Coimbatore 641018 Tamil Nadu India

This communication presents the Procreant pso (particle swarm optimization) algorithm for document-clustering problems. This algorithm is a hybrid of pso and genetic algorithm (GA), a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modelled on the concepts of cultural and social rules derived from the analysis of the swarm intelligence (pso) and also based on crossover and evolution (GA). In standard pso the non-oscillatory route can quickly cause a particle to stagnate and also, it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. In this communication, a modification strategy is proposed for the pso algorithm and applied to the document corpus. The strategy adds reproduction using crossover when stagnation in the movement of the particle is identified and carries out local search to improve the goodness-of-fit. Reproduction has the capability to achieve faster convergence and better solution. Experimental results are examined with document corpus. It demonstrates that the Procreant pso algorithm statistically outperforms the Simple pso and the traditional K-means algorithm.

关键词： Convergence K-means local search Procreant pso algorithm stagnation

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A pso-based Decision Aid for Multi-Aircraft Combat Situations

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2008年第3期10卷 161-167页

作者： Su, Mu-Chun Lai, Shih-Chang Huang, De-Yuan You, Liang-Fu Liao, Yin-Kuei Huang, Jau-Ming Natl Cent Univ Tao Yuan 32001 Taiwan

In this paper, the problem of assigning aircraft to protect assets from attack by enemy aircraft is formulated as a constrained resource allocation problem. The aircraft assignment problem is an NP-complete problem. A pso-based approach incorporating four fuzzy measures of the predominance values for solving the problem is developed to help a military planner reduce the required planning time and provide a better strategy. Several simulations are used to illustrate the effectiveness of the proposed decision aid.

关键词： optimization swarm intelligence pso algorithm genetic algorithm C4ISR

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Volatility forecasting for crude oil based on text information and deep learning pso-LSTM model

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JOURNAL OF FORECASTING 2022年第5期41卷 933-944页

作者： Jiao, Xingrui Song, Yuping Kong, Yang Tang, Xiaolong Shanghai Normal Univ Sch Business & Finance Guilin Rd 1006 A402 Shanghai 200234 Peoples R China Harbin Inst Technol Shenzhen Sch Econ & Management Shenzhen Peoples R China

In this paper, a method to forecast the volatility of crude oil market based on text mining technology and deep learning model is proposed. First, the text mining technology is used to construct the textual features hidden in the news headlines of crude oil market, including the emotional polarity score, investor sentiment classification, and risk factors. Then the textual features with other features reflecting fluctuations of the crude oil market are combined as inputs of the deep learning long short-term memory (LSTM) model for better forecasting results. Furthermore, the particle swarm optimization (pso) is used to optimize the hyper-parameters of the deep learning LSTM model to further improve the forecasting ability. The results in this study show that the LSTM model optimized by pso algorithm has reduced 7.66%, 8.15%, 8.2%, 10.93%, and 23.46% on the mean absolute error (MAE) of crude oil volatility forecasting compared with other models. And it is more stable in multiple-step forecasting results compared with the econometric model. In addition, compared with the pso-LSTM model without textual features, the forecasting accuracy of the pso-LSTM model with textual features has reduced by 6.77% and 5.12% in the MAE and the mean square error (MSE), respectively.

关键词： crude oil news headlines LSTM model market volatility pso algorithm text mining technology

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