{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:48:04Z","timestamp":1760237284763,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T00:00:00Z","timestamp":1585958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Civil Aircraft Project","award":["No. XJ-2015-D-76"],"award-info":[{"award-number":["No. XJ-2015-D-76"]}]},{"name":"key project of the Shaanxi Province key research and development plan","award":["No. 2016MSZD-G-8-1"],"award-info":[{"award-number":["No. 2016MSZD-G-8-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don\u2019t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.<\/jats:p>","DOI":"10.3390\/info11040194","type":"journal-article","created":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T05:05:13Z","timestamp":1586149513000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs"],"prefix":"10.3390","volume":"11","author":[{"given":"Lin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Yian","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6959-6218","authenticated-orcid":false,"given":"Xianchen","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Xuesi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"China Academy of Launch Vehicle Technology, Beijing 100076, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,4]]},"reference":[{"key":"ref_1","first-page":"1554","article-title":"Unmanned Aerial Vehicle","volume":"168","author":"Vijayavargiya","year":"2017","journal-title":"Journal. 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