{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T12:33:45Z","timestamp":1761309225614,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,6,24]],"date-time":"2015-06-24T00:00:00Z","timestamp":1435104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm combined with the least squares method (LMS) to optimize the adaptive network-based fuzzy inference system (ANFIS) model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO) algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks\u2019 cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.<\/jats:p>","DOI":"10.3390\/info6030300","type":"journal-article","created":{"date-parts":[[2015,6,24]],"date-time":"2015-06-24T11:05:12Z","timestamp":1435143912000},"page":"300-313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm"],"prefix":"10.3390","volume":"6","author":[{"given":"Jie-Sheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning,  Anshan 114044, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen-Xu","family":"Ning","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning,  Anshan 114044, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,24]]},"reference":[{"key":"ref_1","first-page":"41","article-title":"Parameters Optimization of ANFIS Based on Particle Swarm Optimization","volume":"20","author":"Wang","year":"2007","journal-title":"J. Petrochem. Univ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1093\/bioinformatics\/bth283","article-title":"Analyzing time series gene expression data","volume":"20","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_3","first-page":"15","article-title":"Study on the time-series modeling of China\u2019s per capita GDP","volume":"11","author":"Dong","year":"2006","journal-title":"Contemp. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/0165-0114(88)90113-3","article-title":"Structure identification of fuzzy model","volume":"28","author":"Sugeno","year":"1988","journal-title":"Fuzzy Sets Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TPWRS.2010.2049385","article-title":"Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting","volume":"26","author":"Catalao","year":"2011","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.enconman.2010.07.015","article-title":"A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal","volume":"52","author":"Pousinho","year":"2011","journal-title":"Energy Convers. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2011.12.015","article-title":"Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm","volume":"83","author":"Meng","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1016\/j.eswa.2008.01.061","article-title":"PSO-based single multiplicative neuron model for time series prediction","volume":"36","author":"Zhao","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6108","DOI":"10.1016\/j.eswa.2008.07.043","article-title":"An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization","volume":"36","author":"Kuo","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.neucom.2009.07.005","article-title":"Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm","volume":"73","author":"Lee","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1016\/j.neucom.2005.12.138","article-title":"Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm","volume":"70","author":"Cai","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e37018","DOI":"10.1371\/journal.pone.0037018","article-title":"An improved PSO algorithm for generating protective SNP barcodes in breast cancer","volume":"7","author":"Chuang","year":"2012","journal-title":"PLoS One"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2013.03.009","article-title":"Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization","volume":"95","author":"Liu","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6047","DOI":"10.1016\/j.eswa.2014.03.053","article-title":"A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting","volume":"41","author":"Samet","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_15","first-page":"1","article-title":"An improved PSO for bankruptcy prediction","volume":"1","author":"Wang","year":"2012","journal-title":"Adv. Comput. Math. Appl."},{"key":"ref_16","first-page":"1636","article-title":"Parameters optimization and implementation of mixed kernels \u025b-SVM based on improved PSO algorithm","volume":"30","author":"Shan","year":"2013","journal-title":"Jisuanji Yingyong Yanjiu"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/6\/3\/300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:20Z","timestamp":1760215700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/6\/3\/300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,24]]},"references-count":17,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2015,9]]}},"alternative-id":["info6030300"],"URL":"https:\/\/doi.org\/10.3390\/info6030300","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2015,6,24]]}}}