{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:38:16Z","timestamp":1760236696932,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12105120"],"award-info":[{"award-number":["12105120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["19KJB160001"],"award-info":[{"award-number":["19KJB160001"]}]},{"name":"Lianyungang city Haiyan project","award":["2019-QD-004"],"award-info":[{"award-number":["2019-QD-004"]}]},{"name":"Open Fund Project of Jiangsu Institute of Marine Resources Development","award":["JSIMR202018"],"award-info":[{"award-number":["JSIMR202018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi\u2019s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.<\/jats:p>","DOI":"10.3390\/sym14010011","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Intelligent Dendritic Neural Model for Classification Problems"],"prefix":"10.3390","volume":"14","author":[{"given":"Weixiang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China"}]},{"given":"Dongbao","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China"},{"name":"The Ministry of Education Key Laboratory of TianQin Project, Sun Yat-sen University, Zhuhai 519082, China"}]},{"given":"Zhaoman","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China"}]},{"given":"Cunhua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China"}]},{"given":"Zhongxun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Evaluation of classification models in machine learning","volume":"7","year":"2017","journal-title":"Theory Appl. 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