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According to the dynamic risk characteristics which is complex and uncertain, DRIBIEM dynamically maps the intensity and frequency of risk to the concentration of antigen, based on the cell death pattern, stimulates immune memory, guides the evolution of antibodies and controls life cycle of identifier by antigen concentration which solves the problem that the traditional immune identification algorithm takes too long time, realizes the distributed automatic updating of identifiers and improves the dynamic risk identification ability. Simulation results show that DRIBIEM fully reflects the dynamic characteristics of immune memory and can effectively identify the complex dynamic risks. Its feasibility can be verified in the practical application of dynamic risk identification.<\/jats:p>","DOI":"10.3233\/jifs-169720","type":"journal-article","created":{"date-parts":[[2018,7,17]],"date-time":"2018-07-17T12:14:18Z","timestamp":1531829658000},"page":"3971-3985","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic memory risk identification model and simulation based on immune algorithm extension"],"prefix":"10.1177","volume":"35","author":[{"given":"Bo","family":"Yang","sequence":"first","affiliation":[{"name":"JiangXi University of Finance and Economics, School of Information Management, Nanchang, China"}]},{"given":"Yang-an","family":"Chen","sequence":"additional","affiliation":[{"name":"JiangXi University of Finance and Economics, School of Information Management, Nanchang, China"}]}],"member":"179","published-online":{"date-parts":[[2018,7,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.08.024"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2003.12.008"},{"issue":"7","key":"e_1_3_2_4_2","first-page":"29","article-title":"Artificial immune memory model","volume":"16","author":"Li T.","year":"2006","unstructured":"LiT., LiuS. and ShuB., Artificial immune memory model, Computer Technology and Development16(7) (2006), 29\u201331.","journal-title":"Computer Technology and Development"},{"issue":"4","key":"e_1_3_2_5_2","first-page":"49","article-title":"Unconventional emergency risk identification model based on the immune system","volume":"18","author":"Yang Q.","year":"2015","unstructured":"YangQ. and LiuX.X., Unconventional emergency risk identification model based on the immune system, Journal of Management Sciences in China18(4) (2015), 49\u201361.","journal-title":"Journal of Management Sciences in China"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0773-52"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2007.02.047"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1360\/jos182700"},{"issue":"18","key":"e_1_3_2_9_2","first-page":"1","article-title":"A new mutation detection method based on fisher information theory","volume":"62","author":"Cai S.-P.","year":"2013","unstructured":"CaiS.-P. and DaiL., A new mutation detection method based on fisher information theory, Journal of Physics62(18) (2013), 1\u20139.","journal-title":"Journal of Physics"},{"issue":"18","key":"e_1_3_2_10_2","first-page":"1","article-title":"Study on the critical early warning of mean mutation time series based on logistic model","volume":"61","author":"Yan P.-C.","year":"2012","unstructured":"YanP.-C., HouW. and HuJ.-G., Study on the critical early warning of mean mutation time series based on logistic model, Journal of Physics61(18) (2012), 1\u20138.","journal-title":"Journal of Physics"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1108\/17563781011049188"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85072-4_6"},{"key":"e_1_3_2_13_2","first-page":"386","volume-title":"Modeling Immune Memory for Prediction and Computation","author":"Wilson W.O.","year":"2004","unstructured":"WilsonW.O. and GarrettS.M., Modeling Immune Memory for Prediction and Computation, 3rd International Conference in Artificial Immune Systems, ICARISCatania, Sicily, Italy: Springer, 2004, pp. 386\u2013399."},{"key":"e_1_3_2_14_2","unstructured":"Chapter 15 cell aging and apoptosis[DB\/OL]. http:\/\/www.cella.cn\/book\/15\/02.htm"},{"issue":"12","key":"e_1_3_2_15_2","first-page":"1281","article-title":"Artificial immune system: Principle, models, analysis and perspectives","volume":"25","author":"Xiao R.B.","year":"2002","unstructured":"XiaoR.B., Artificial immune system: Principle, models, analysis and perspectives, Chinese Journal of Computers25(12) (2002), 1281\u20131293.","journal-title":"Chinese Journal of Computers"},{"issue":"1","key":"e_1_3_2_16_2","first-page":"217","article-title":"A dynamic multi-objective artificial immune system model based on evolution and immunity","volume":"37","author":"Tao Y.","year":"2010","unstructured":"TaoY., WuG. and HuM., A dynamic multi-objective artificial immune system model based on evolution and immunity, Computer Science37(1) (2010), 217\u2013222.","journal-title":"Computer Science"},{"key":"e_1_3_2_17_2","first-page":"36","volume-title":"GECCO 2002-Workshop Proceedings, 2000","author":"De Castro L.N.","year":"2000","unstructured":"De CastroL.N. and Von ZubenF.J., Clonal selection algorithm with engineering applications, GECCO 2002-Workshop Proceedings, 2000. 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