{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:08:16Z","timestamp":1740103696728,"version":"3.37.3"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"],"award-info":[{"award-number":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"],"award-info":[{"award-number":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002703","name":"Jiangsu University","doi-asserted-by":"publisher","award":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"],"award-info":[{"award-number":["JCYJ20160428092427867","2017GKTSCX060","2021SJA0486"]}],"id":[{"id":"10.13039\/501100002703","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2023,7,18]]},"abstract":"<jats:p>In this paper, the<jats:inline-formula><a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><a:mi>p<\/a:mi><a:mi mathvariant=\"normal\">t<\/a:mi><a:mi mathvariant=\"normal\">h<\/a:mi><\/a:math><\/jats:inline-formula>moment exponential synchronization problems of drive-response stochastic memristor neural networks are studied via a state feedback controller. The dynamics of the memristor neural network are nonidentical, consisting of both asymmetrically nondelayed and delayed coupled, state-dependent, and subject to exogenous stochastic perturbations. The pth moment exponential synchronization of these drive-response stochastic memristor neural networks is guaranteed under some testable and computable sufficient conditions utilizing differential inclusion theory and Filippov regularization. Finally, the correctness and effectiveness of our theoretical results are demonstrated through a numerical example.<\/jats:p>","DOI":"10.1155\/2023\/1335184","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T23:35:11Z","timestamp":1689723311000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["The pth Moment Exponential Synchronization of Drive-Response Memristor Neural Networks Subject to Stochastic Perturbations"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6810-8945","authenticated-orcid":true,"given":"Xiaobo","family":"Wang","sequence":"first","affiliation":[{"name":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China"},{"name":"Business School, Nanjing Xiaozhuang University, Nanjing 211171, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4220-8005","authenticated-orcid":true,"given":"Xuefei","family":"Wu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence School, Shenzhen Polytechnic, Shenzhen 518055, China"}]},{"given":"Zhe","family":"Nie","sequence":"additional","affiliation":[{"name":"Digital Creativity and Animation School, Shenzhen Polytechnic, Shenzhen 518055, China"}]},{"given":"Zengxian","family":"Yan","sequence":"additional","affiliation":[{"name":"Guangxi Modern Polytechnic College, Hechi 547000, Guangxi, China"}]}],"member":"311","reference":[{"issue":"7","key":"1","doi-asserted-by":"crossref","DOI":"10.1088\/0957-4484\/23\/7\/075201","article-title":"High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm","volume":"23","author":"F. 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