{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:02Z","timestamp":1760188562425,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.<\/jats:p>","DOI":"10.3390\/a12050097","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A New Method of Applying Data Engine Technology to Realize Neural Network Control"],"prefix":"10.3390","volume":"12","author":[{"given":"Song","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China"},{"name":"Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou University, Fuzhou 350116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Bi","sequence":"additional","affiliation":[{"name":"Research Institute of Fujian Histron Group Co., Ltd., Fuzhou 350116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilin","family":"Song","sequence":"additional","affiliation":[{"name":"Research Institute of Fujian Histron Group Co., Ltd., Fuzhou 350116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","first-page":"36","article-title":"DC motor position control using fuzzy proportional-derivative controllers with different defuzzication methods","volume":"1","author":"Namazov","year":"2010","journal-title":"TJFS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1080\/00207543.2016.1173259","article-title":"Advanced control in factory automation: A survey","volume":"55","author":"Dotoli","year":"2017","journal-title":"Int. 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