{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:36:03Z","timestamp":1773387363543,"version":"3.50.1"},"reference-count":57,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Nos. 62176216 and 62076206"],"award-info":[{"award-number":["Nos. 62176216 and 62076206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Research Fund of Sichuan Science and Technology","award":["No. 2022ZYD0115"],"award-info":[{"award-number":["No. 2022ZYD0115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p>Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems have a nonlinear structure and can show rich nonlinear dynamics. In this paper, we introduce a variant of NSNP systems, called gated nonlinear spiking neural P systems or GNSNP systems. Based on GNSNP systems, a recurrent-like model is investigated, called GNSNP model. Moreover, exchange rate forecasting tasks are used as the application background to verify its ability. For the purpose, we develop a prediction model based on GNSNP model, called ERF-GNSNP model. In ERF-GNSNP model, the GNSNP model is followed by a \u201cdense\u201d layer, which is used to capture the correlation between different sub-series in multivariate time series. To evaluate the prediction performance, nine groups of exchange rate data sets are utilized to compare the proposed ERF-GNSNP model with 25 baseline prediction models. The comparison results demonstrate the effectiveness of the proposed ERF-GNSNP model for exchange rate forecasting tasks.<\/jats:p>","DOI":"10.1142\/s0129065723500296","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T04:24:40Z","timestamp":1679631880000},"source":"Crossref","is-referenced-by-count":46,"title":["A Prediction Model Based on Gated Nonlinear Spiking Neural Systems"],"prefix":"10.1142","volume":"33","author":[{"given":"Yujie","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"given":"Qian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"given":"Zhicai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"given":"Hong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"S0129065723500296BIB001","first-page":"279","volume":"71","author":"Ionescu M.","year":"2006","journal-title":"Fundam. 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