{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:41:39Z","timestamp":1762389699614,"version":"3.37.3"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51407144","51777169"],"award-info":[{"award-number":["51407144","51777169"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004750","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["20164053029"],"award-info":[{"award-number":["20164053029"]}],"id":[{"id":"10.13039\/501100004750","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["3102017ZY027","3102017GX08001"],"award-info":[{"award-number":["3102017ZY027","3102017GX08001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Young Elite Scientist Sponsorship Program by CAST","award":["2015QNRC001"],"award-info":[{"award-number":["2015QNRC001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight training algorithm in neural network application, the back propagation (BP) neural network learning algorithm combined with simulated annealing genetic algorithm (SAGA) is put forward. The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations. The simulated annealing mechanism is incorporated into the Genetic Algorithm (GA) to optimize the design and optimization of neural network conformation and network weights simultaneously. The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process, also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon. The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm. The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness. <\/jats:p>","DOI":"10.1142\/s1793962319500247","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T22:29:33Z","timestamp":1564439373000},"page":"1950024","source":"Crossref","is-referenced-by-count":28,"title":["Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis"],"prefix":"10.1142","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6366-0848","authenticated-orcid":false,"given":"Dawei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation, Department of Electrical Engineering, Northwestern Polytechnical University, 710129 Xi\u2019an, Shaanxi, P. R. China"},{"name":"School of Basic Science for Aviation, Naval Aviation University, 264001 Yantai, Shandong, P. R. 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