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This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high\u2010degree cubature Kalman filter. The high\u2010degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.1155\/2021\/5124841","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T17:20:06Z","timestamp":1624555206000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9699-7006","authenticated-orcid":false,"given":"Daxing","family":"Xu","sequence":"first","affiliation":[]},{"given":"Aiyu","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Xuelong","family":"Han","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2880-8306","authenticated-orcid":false,"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7851080"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/7518039"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/iconraeece.2011.6129799"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6641758"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.2174\/2213111607666190215121814"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10040448"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tfuzz.2009.2029569"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929266"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015200"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2846483"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2020.590371"},{"key":"e_1_2_8_12_2","unstructured":"HsuC.W. 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