Computer Science ›› 2023, Vol. 50 ›› Issue (5): 292-301.doi: 10.11896/jsjkx.220300259
• Artificial Intelligence • Previous Articles Next Articles
ZHU Xuhui1,2, SHE Xiaomin1,2, NI Zhiwei1,2, XIA Pingfan1,2, ZHANG Chen3
CLC Number:
[1]PADILLA W R,GARCÍA J,MOLINA J M.Improving time series forecasting using information fusion in local agricultural markets[J].Neurocomputing,2021,452:355-373. [2]TAI V V,CHENGOC H,LEDAI N,et al.A new strategy for short-term stock investment using bayesian approach[J].Computational Economics,2021,59(2):887-911. [3]DHANAPAL R,AJANRAJ A,BALAVINAYAGAPRAGATHI-SH S,et al.Crop price prediction using supervised machine learning algorithms[J].Journal of Physics:Conference Series,2021,1916(1):012042. [4]YANG B Q,ZHANG X L.Forecast of price of rare earths neodymium oxide and dysprosium oxide based on ARIMA time series model[J].Journal of the Chinese Society of Rare Earths,2017,35(5):680-686. [5]DU Y A.Research on the route pricing optimization model ofthe car-free carrier platform based on the BP neural network algorithm[J].Complexity,2021,2021(4):8204214. [6]E J W,YE J M,HE L L,et al.A denoising carbon price foreca-sting method based on the integration of kernel independent component analysis and least squares support vector regression[J].Neurocomputing,2021,434:67-79. [7]HUANG G B,WANG D H,LAN Y.Extreme learning ma-chines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122. [8]XU H X,MA C L,FENG H.A thrust allocation method based on extreme learning machine[J].Journal of Huazhong Univer-sity of Science and Technology(Natural Science Edition),2021,49(12):34-39,70. [9]HUO Y L,LI Y L.A plant leaf classification method based on multi feature fusion and extreme learning machine[J].Computer Engineering and Science,2021,43(3):486-493. [10]WANG H X,CHEN Y Q,SHEN J,et al.Novel semi-supervised extreme learning machine and its application in anti-vibration hammer corrosion detection[J].Computer Science,2020,47(12):262-266. [11]LIU W,YAN S,CHEN T,et al.Feature recognition of irregular pellet images by regularized extreme learning machine in combination with fractal theory[J].Future Generation Computer Systems,2022,127:92-108. [12]LI L L,SUN J,TSENG M L,et al.Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation[J].Expert Systems with Applications,2019,127:58-67. [13]SUBUDHI U,DASH S.Detection and classification of powerquality disturbances using GWO ELM[J].Journal of Industrial Information Integration,2021,22:100204. [14]MUDULI D,DASH R,MAJHI B.Automated breast cancer detection in digital mammograms:A moth flame optimization based ELM approach[J].Biomedical Signal Processing and Control,2020,59:101912. [15]MIRJALILI S,GANDOMI A H,MIRJALILI S Z,et al.SalpSwarm Algorithm:A bio-inspired optimizer for engineering design problems[J].Advances in Engineering Software,2017,114:163-191. [16]LIU J S,YUAN M M,ZUO F.Global search-oriented adaptive leader salp swarm algorithm[J].Control and Decision,2021,36(9):2152-2160. [17]YU J S,WU L.Two types of leaders salp swarm algorithm[J].Computer Science,2021,48(4):254-260. [18]KAMEL S,EBEED M,JURADO F,et al.An improved version of salp swarm algorithm for solving optimal power flow problem[J].Soft Computing,2021,25(5):4027-4052. [19]HEGAZY A E,MAKHLOUF M A,El-Tawel G S.Feature selection using chaotic salp swarm algorithm for data classification[J].Arabian Journal for Science and Engineering,2019,44(4):3801-3816. [20]BALAKRISHNAN K,DHANALAKSHMI R,KHAIRE U M.Improved salp swarm algorithm based on the levy flight for feature selection[J].The Journal of Supercomputing,2021,77(11):12399-12419. [21]CHAABANE S B,BELAZI A,KHARBECH S,et al.Improved salp swarm optimization algorithm:application in feature weighting for blind modulation identification[J].Electronics,2021,10(16):2002. [22]LI Y C,HAN M X,GUO Q L.Modified whale optimization algorithm based on tent chaotic mapping and its application in structural optimization[J].KSCE Journal of Civil Engineering,2020,24(12):3703-3713. [23]WANG M N,WANG Q P,WANG X F.Improved grey wolf optimization algorithm based on iterative mapping and simplex method[J].Journal of Computer Applications,2018,38(A2):16-20,54. [24]XIA P F,NI Z W,ZHU X H.Attribute selection method based on fireworks evolution artificial fish swarm algorithm and multi-fractal dimension with its application in air quality prediction[J].Journal of Systems Science and Mathematical Sciences,2020,40(7):1157-1177. [25]PENG P,NI W,ZHU X H,et al.Attribute reduction methodbased on improved binary glowworm swarm optimization algorithm and neighborhood rough set[J].Pattern Recognition and Artificial Intelligence,2020,33(2):95-105. [26]DONG H B,PANG J W,HAN Q L.Gray extreme learning machine prediction method[J].Computer Science,2015,42(5):78-81,105. [27]BARATA J C A,HUSSEIN M S.The Moore-Penrose pseudoinverse:A tutorial review of the theory[J].Brazilian Journal of Physics,2012,42(1):146-165. [28]WANG J.Research on the linkage relationship between coal-coke-iron prices[J].Coal Economic Research,2008,2:13-16. [29]LAURITZEN S L.The EM algorithm for graphical association models with missing data[J].Computational Statistics & Data Analysis,1995,19(2):191-201. [30]AZLI H,TITRI S,LARBES C.MPPT-Based improved salpswarm algorithm for improving performance and efficiency of photovoltaic system under partial shading condition[C]//ICAIRES 2020:Artificial Intelligence and Renewables Towards an Energy Transition.2020:478-486. [31]MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67. [32]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61. [33]SHEHAB M,ABUALIGAH L,AL HAMAD H,et al.Moth-flame optimization algorithm:variants and applications[J].Neural Computing and Applications,2020,32(14):9859-9884. [34]XIA P F,NI Z W,ZHU X H,et al.Selective ensemble approach based on reverse binary glowworm swarm optimization and diversity measure[J].Journal of Systems Science and Mathematical Sciences,2021,41(3):730-746. |
[1] | QING Chao-jin, DU Yan-hong, YE Qing, YANG Na, ZHANG Min-tao. Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors [J]. Computer Science, 2022, 49(6A): 632-638. |
[2] | XU Kun-cai, FENG Bao, CHEN Ye-hang, LIU Yu, ZHOU Hao-yang, CHEN Xiang-meng. Thymoma CT Image Prediction Method Based on Deep Learning and Improved Extreme Learning Machine Ensemble Learning [J]. Computer Science, 2022, 49(11A): 211200097-6. |
[3] | HE Yu-lin, LI Xu, JIN Yi, HUANG Zhe-xue. Handwritten Character Recognition Based on Decomposition Extreme Learning Machine [J]. Computer Science, 2022, 49(11): 148-155. |
[4] | XU Si-qin, HUANG Xiang-qian, YANG Kun, ZHANG Zhan-long, GAN Peng-fei. Prediction of Insulation Deterioration Degree of Cable Joints Based on Temperature and Operation Data [J]. Computer Science, 2022, 49(10): 132-137. |
[5] | LUO Wen-cong, ZHENG Jia-li, QUAN Yi-xuan, XIE Xiao-de, LIN Zi-han. Optimized Deployment of RFID Reader Antenna Based on Improved Multi-objective Salp Swarm Algorithm [J]. Computer Science, 2021, 48(9): 292-297. |
[6] | XIANG Chang-sheng, CHEN Zhi-gang. Chaotic Prediction Model of Network Traffic for Massive Data [J]. Computer Science, 2021, 48(5): 289-293. |
[7] | YU Jia-shan, WU Lei. Two Types of Leaders Salp Swarm Algorithm [J]. Computer Science, 2021, 48(4): 254-260. |
[8] | ZHOU Chuan. Optimization of Sharing Bicycle Density Distribution Based on Improved Salp Swarm Algorithm [J]. Computer Science, 2021, 48(11A): 106-110. |
[9] | ZHANG Zhi-qiang, LU Xiao-feng, SUI Lian-sheng, LI Jun-huai. Salp Swarm Algorithm with Random Inertia Weight and Differential Mutation Operator [J]. Computer Science, 2020, 47(8): 297-301. |
[10] | ZHANG Yan, QIN Liang-xi. Improved Salp Swarm Algorithm Based on Levy Flight Strategy [J]. Computer Science, 2020, 47(7): 154-160. |
[11] | WANG Jun-hao, YAN De-qin, LIU De-shan, XING Yu-jia. Algorithm with Discriminative Analysis Dictionary Learning by Fusing Extreme Learning Machine [J]. Computer Science, 2020, 47(5): 137-143. |
[12] | WANG Hong-xing, CHEN Yu-quan, SHEN Jie, ZHANG Xin, HUANG Xiang, YU Bin. Novel Semi-supervised Extreme Learning Machine and its Application in Anti-vibration HammerCorrosion Detection [J]. Computer Science, 2020, 47(12): 262-266. |
[13] | GUO Wei, YU Jian-jiang, TANG Ke-ming, XU Tao. Survey of Online Sequential Extreme Learning Algorithms for Dynamic Data Stream Analysis [J]. Computer Science, 2019, 46(4): 1-7. |
[14] | WANG Zhe, ZHENG Jia-li, LI Li, YUAN Yuan, SHI Jing. RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine [J]. Computer Science, 2019, 46(12): 120-125. |
[15] | XING Yi-ming, BAN Xiao-juan, LIU Xu, YIN Hang, SHEN Qing. Traffic Congestion Prediction Based on Kernel Extreme Learning Machine Group Algorithm [J]. Computer Science, 2019, 46(11): 241-246. |
|