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intelligent greedy pursuit model for sparse reconstruction based on l₀ minimization

SIGNAL PROCESSING 2016年 122卷 138-151页

作者： Li, Dan Wang, Qiang Shen, Yi Harbin Inst Technol Control Sci & Engn 92 West Da Zhi St Harbin 150001 Peoples R China

l(0) minimization based sparse reconstruction is an NP-hard problem with very high computational complexity, which is difficult to be achieved by traditional algorithms. Although greedy algorithm aims at solving l(0) minimization, it is more likely to obtain a sub-optimal solution. In this paper, we propose an intelligent greedy pursuit (IGP) algorithm to solve the l(0) minimization essentially. Firstly, we propose a novel optimization function for the sparse reconstruction problem with the sparsity level unknown as a prior. Then, a two-cycle optimization algorithm is designed, whose object is to estimate the support collection and its corresponding coefficients intelligently and accurately by searching for the global optimal solution. To this end, we take advantage of intelligent optimization algorithm in global searching and solving combinatorial optimization problems to guide the intelligent estimation. Also, the principle of estimation is designed by the matching strategies of greedy algorithm which performs quite well in reconstruction speed. The so-called IGP model is as simple as greedy algorithm, while it has been proved through experiments that the performance of IGP for signal reconstruction and image reconstruction outperforms the state-of-the-art reconstruction algorithms. Moreover, IGP can reconstruct signal accurately with a relatively small measurement rate. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Compressive sensing l(0) minimization intelligent optimization algorithm Greedy algorithm

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Simulation of Assembly Tolerance and Characteristics of High Pressure Common Rail Injector

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2011年第6期4卷 1282-1289页

作者： Lu, Jiping Tang, Shuiyuan Zhou, Yong Hong, Jing Jian, Zhonghua Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China CNGC ShanXi Diesel Engine Ind CO LTD Datong 037036 Peoples R China China N Ind Grp Co Inst Sci & Technol Beijing 100089 Peoples R China

Fuel injector is the key part of a high-pressure common rail fuel injection system. Its manufacturing precision and assembly quality affect system's property and performance. According to the characteristics and demands of assembly of the fuel injector, an intelligent optimization algorithm is proposed to resolve the problem of assembly sequence planning. Based on geometric modeling, assembly dimension chain of the injector control chamber is established, and the relationship between assembly tolerance and volume change of control chamber is analyzed. The optimization model of the assembly is established. The impact of assembly tolerance on injector's performance is simulated according to the optimization algorithm. The simulation result shows that quantity of injection fuel changes correspondingly with the change of assembly tolerance, while injection rate and pressure do not change significantly, and the response rate of needle considerably slow. Similarly, the leakage rate of fuel in control chamber is calculated, indicating that the assembly tolerance has obvious impact on fuel leakage and its rate. The study illuminates that injector's assembly tolerance has prominent effect on injection.

关键词： assembly tolerance intelligent optimization algorithm high pressure common rail simulation

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Predicted multi-variable intelligent matching pursuit algorithm for image sequences reconstruction based on l₀ minimization

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 38卷 316-327页

作者： Li, Dan Wang, Qiang Shen, Yi Harbin Inst Technol Control Sci & Engn 92 West Da Zhi St Harbin 150001 Peoples R China

In this paper, we study the problem of reconstructing image sequences which satisfy the conditions that (a) the sparsity level is high in the wavelet domain and (b) the sparsity pattern of adjacent images changes very slowly. The idea of the proposed method predicted multi-variable intelligent matching pursuit (PMIMP) algorithm is to use the estimated support collection of the previous image as prior information and then utilize the prior information to guide the current image reconstruction by solving 10 minimization. Multi-variable scheme is used to sample image sequences to enhance the guidance of prior information and improve the reconstruction accuracy with fewer measurements. to minimization is an NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to be achieved by traditional algorithms. To solve it, we take advantage of the intelligent optimization algorithm which is famous for its global searching ability and superior performance in solving combinatorial optimization problems. To improve the reconstruction speed, matching strategies of greedy algorithm, which performs quite well in reconstruction speed, are utilized to design the updating mechanism of PMIMP. As the sparsity level is hard to be estimated in image sequences reconstruction, we propose a novel optimization function which does not need the sparsity level known as a prior. We illustrate the reconstruction performance of our proposed method PMIMP on several image sequences and compare it with the state-of-the-art algorithms. The experimental results demonstrate that PMIMP achieves the best reconstruction performance in both PSNR, SSIM and visual quality with fewer measurements. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Image sequences reconstruction Prior information Multi-variable sampling l(0) minimization intelligent optimization algorithm Matching strategies

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New optimization Design Method for a Double Secondary Linear Motor Based on R-DNN Modeling Method and MCS optimization algorithm

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Chinese Journal of Electrical Engineering 2020年第3期6卷 98-105页

作者： Weitao Wang Jiwen Zhao Yang Zhou Fei Dong School of Electrical Engineering and Automation Anhui UniversityHefei 230601China School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China

Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization ***,this approach has disadvantages,e.g.,the analytical model might not be accurate enough,and the intelligent optimization algorithm can easily fall into local optimization.A new linear motor optimization strategy combining an R-deep neural network(R-DNN)and modified cuckoo search(MCS)is proposed;additionally,the thrust lifting and thrust fluctuation reductions are regarded as optimization *** R-DNN is a deep neural network modeling method using the rectified linear unit(RELU)activation function,and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms,particle swarm optimization,and standard CS ***,the validity and accuracy of this work are proven based on prototype experiments.

关键词： Double secondary linear motor(DSLM) machine learning modeling R-deep neural network(R-DNN)algorithm intelligent optimization algorithm modified cuckoo search(MCS)algorithm

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Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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Big Data Mining and Analytics 2023年第3期6卷 361-380页

作者： Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv College of Computer Science Sichuan UniversityChengdu 610041China Algorithm and Big Data Center New Hope Liuhe Co LtdChengdu 610000China. Data Intelligence Lab New Hope Liuhe Co LtdChengdu 610000China. College of Computer Science Sichuan UniversityChengdu 610041China.

Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in *** paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up *** with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large *** methods applied in product yield analysis build models based on massive *** fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model *** methods applied in flue gas desulfurization can be usually divided into two stages:modeling and *** the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule *** methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition ***-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction *** the data-driven methods are just the ***,some studies combine these two methods to obtain better results.

关键词： neural networks intelligent optimization algorithm catalytic cracking lumped kinetics

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A hybrid adaptive-prediction maximum power point tracking method for the smart city with massive photovoltaic

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ENERGY REPORTS 2022年 8卷 240-251页

作者： Deng, Wenyang Chen, Chuyue Zhang, Yongjun Cheng, Runting Dai, Siting Zhou, Xingyue South China Univ Technol Sch Elect Power Guangzhou 514000 Peoples R China City Univ Macau Fac Finance Taipa 999078 Macao Peoples R China

The partial shadow condition seriously affects the efficiency of the photovoltaic system in the modern city with dense built and other occlusions. From this, the characteristic curve of the photovoltage system shows multi-peak, which further increases the difficulty of getting photovoltaic systems to operate at maximum efficiency. As an efficient technique, the intelligent optimized maximum power point tracking method relies on initialization information and is difficult to balance the tracking performance. Therefore, a hybrid adaptive-prediction maximum power tracking method is proposed in this paper. Firstly, the neighborhood range of the maximum power points is located by the fuzzy predicted mechanism at the upper layer. Secondly, on the bottom layer, based on improving the Cuckoo search algorithm, the proposed method uses an interpolation function fitting curve to guide the particles to converge accurately on the bottom layer. At the same time, the output voltage of the system under an open loop is directly controlled by the duty cycle of the control signal, which improves the universality of the method. Finally, the simulation results show that the proposed method is superior to other advanced methods in tracking speed and with smaller power oscillations and comparable tracking accuracy, for which the proposed method is suitable for the city with complex environments and dense buildings. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://***/licenses/by/4.0/).

关键词： Partial shadow conditions intelligent optimization algorithm Adaptive prediction Improved cuckoo algorithm

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A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

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JOURNAL OF CLEANER PRODUCTION 2024年 449卷

作者： Yang, Hong Wu, Qingsong Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi -stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi -scale dispersion entropy (RCMDE), empirical mode decomposition based on time -varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN -EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU - CNN - EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high -complexity and low -complexity components by RCMDE. Next, secondly decompose the reconstructed highcomplexity components into high -complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high -complexity parts and the low -complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

关键词： Tidal energy Predict Mode decomposition intelligent optimization algorithm Forecasting Error correction

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Hybrid algorithm for multi-objective optimization design of parallel manipulators

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APPLIED MATHEMATICAL MODELLING 2021年 98卷 245-265页

作者： Chen, Qiaohong Yang, Chao Zhejiang Sci Tech Univ Sch Informat Hangzhou 310018 Zhejiang Peoples R China Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Zhejiang Peoples R China

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regres-sion and particle swarm optimization algorithm. For the proposed method, the global per-formance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objec-tive functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process re-gression and particle swarm optimization algorithm was proposed for multi-objective op-timization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint;R a revolute joint;U a universal joint). The comparison from the back propagation neural network, multivariate regres-sion, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization, the performance i

关键词： Parallel manipulator Mapping modeling Gaussian process regression intelligent optimization algorithm

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Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第14期237卷 3210-3220页

作者： Li, Hang Hu, Xiaobing Zhang, Xuejian Wei, Shangyun Luo, Qingyi Sichuan Univ Sch Mech Engn Chengdu Peoples R China Sichuan Univ Yibin R&D Pk Yibin Peoples R China Sichuan Univ Sch Mech Engn 24South Sect 1First Ring Rd Chengdu 610065 Peoples R China

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three (x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

关键词： Industrial robot positioning accuracy kinematic parameters calibration intelligent optimization algorithm laser tracker

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Stochastic Computing and intelligent optimization Analysis for a Generalized Information Delivery System

Stochastic Computing and Intelligent Optimization Analysis f...

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2019 International Conference on Management, Education Technology and Economics(ICMETE 2019)

作者： Xiao Xiaonan Xiamen University Tan Kah Kee College

In this paper, we use the optimal nonlinear random filtering method and intelligent optimization algorithm to study the optimal control problem of a kind of incomplete data and continuous nonstationary stochastic information delivery system. We obtain the two optimal control mathematical models in these two situations;illustrate how to establish the optimal encoding and decoding of the nonstationary stochastic process;and provide an effective and reliable approach for the optimal control of such a process.

关键词： Generalized information Delivery system Stochastic analysis intelligent optimization algorithm Optimal control

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