{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:19:19Z","timestamp":1771679959511,"version":"3.50.1"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":88,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["lzujbky-2020-kb25"],"award-info":[{"award-number":["lzujbky-2020-kb25"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61931019"],"award-info":[{"award-number":["61931019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>On the basis of the chaotic features of the frequency hopping signal, frequency band prediction for frequency hopping signal can enhance the interference effect of the signal greatly. However, poor prediction accuracy often limits its development in the military field. Therefore, for the sake of enhancing the frequency band prediction accuracy of frequency hopping signal, this paper studies the radial basis function (RBF) neural network frequency hopping signal frequency band prediction model based on the gradient descent method and improved the particle swarm optimization algorithm, respectively. The former uses a step\u2010by\u2010step algorithm to optimize the center value and weight so that the network can find the most suitable initial state. Then, the clustering selection optimization algorithm is employed to optimize the central value. In addition, it optimizes the weight by using a gradient descent method of the optimal learning rate. The latter optimizes the structure of the RBF neural network through the combination of the subtractive clustering algorithm and improved the particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the gradient RBF algorithm model performs better in terms of accuracy, but time efficiency is lower, while the PSO\u2010RBF algorithm has better time efficiency.<\/jats:p>","DOI":"10.1155\/2021\/5570685","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:05:08Z","timestamp":1617149108000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RBF Neural Network\u2010Based Frequency Band Prediction for Future Frequency Hopping Communications"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7633-7396","authenticated-orcid":false,"given":"Shengyan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7875-1395","authenticated-orcid":false,"given":"Yongjian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8286-0383","authenticated-orcid":false,"given":"Jianbo","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2799-4100","authenticated-orcid":false,"given":"Shupeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.2995856"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3040782"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2009.10.004"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.3008935"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2020.3034976"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3046000"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"GaoS. SunJ. andGaoX. Soft-sensor modeling of rectification of vinyl chloride based on improved PSO-RBF neural network 2012 24th Chinese Control and Decision Conference (CCDC) 2012 Taiyuan China.","DOI":"10.1109\/CCDC.2012.6244179"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2002.1000134"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3026732"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"XuM. ChenH. andDuanL. A combined training algorithm for RBF neural network based on particle swarm optimization and gradient descent 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) 2020 Liuzhou China.","DOI":"10.1109\/DDCLS49620.2020.9275049"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3023936"},{"key":"e_1_2_9_12_2","first-page":"1","article-title":"Scholar2vec: vector representation of scholars for lifetime collaborator prediction","author":"Wang W.","year":"2020","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3053136"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"ChenJ. Y. QinZ. andJiaJ. A PSO-based subtractive clustering technique for designing RBF neural networks IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) 2008 Hong Kong China 2047\u20132052.","DOI":"10.1109\/CEC.2008.4631069"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3025116"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/AMR.463-464.922"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3020645"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"Xiang-JunD.andYan-QinW. RBF neural network identifier based on optimal selection cluster algorithm and PSO algorithm and its application 2011 Fourth International Conference on Intelligent Computation Technology and Automation 2011 Shenzhen China 884\u2013887.","DOI":"10.1109\/ICICTA.2011.222"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-009-9095-3"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3125-y"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/731368"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2997832"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"WenxianG. HongxiangW. JianxinX. andYunfengZ. RBF neural network model based on improved PSO for predicting river runoff International Conference on Intelligent Computation Technology & Automation IEEE Computer Society 2010 Changsha China.","DOI":"10.1109\/ICICTA.2010.504"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.2970276"},{"key":"e_1_2_9_25_2","unstructured":"ZhangN. Urban stormwater runoff prediction using recurrent neural networks Advances in Neural Networks-ISNN 2011-8th International Symposium on Neural Networks ISNN 2011 2011 Guilin China May 29-June 1 2011 Proceedings Part I. DBLP."},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/11893257_125"},{"key":"e_1_2_9_27_2","volume-title":"Intelligent Resource Allocation in Mobile Blockchain for Privacy and Security Transactions: A Deep Reinforcement Learning Based Approach","author":"Ning Z.","year":"2020"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3012509"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/5570685.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/5570685.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5570685","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T13:36:17Z","timestamp":1723037777000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5570685"}},"subtitle":[],"editor":[{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5570685"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5570685","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"value":"1530-8669","type":"print"},{"value":"1530-8677","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-01-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5570685"}}