{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T18:09:33Z","timestamp":1761156573966,"version":"3.41.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"content-version":"vor","delay-in-days":294,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>The neural network has the advantages of self\u2010learning, self\u2010adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS\u2010RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi\u2010information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS\u2010RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self\u2010organizing RBF neural networks (self\u2010organizing RBF (SORBF) and rough RBF neural networks (RS\u2010RBF)), VS\u2010RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS\u2010RBF.<\/jats:p>","DOI":"10.1155\/2018\/6950124","type":"journal-article","created":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T23:38:54Z","timestamp":1540251534000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4525-1415","authenticated-orcid":false,"given":"Tiantian","family":"Luan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6528-919X","authenticated-orcid":false,"given":"Mingxiao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daidai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"volume-title":"USS Nimitz and Carrier Airwing Nine Surge Demonstration","year":"1998","author":"Jewell A.","key":"e_1_2_10_1_2"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/2678216"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169191"},{"volume-title":"An Evaluation of the Suitability of LCOM for Modeling. 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