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Assistant decision-making model has been constructed after analysis on collaborative intercepting principle; then Improved Clonal Selection Algorithm Optimizing Neural Network (ICLONALG-NN) is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity, introducing modified combination operator to make use of the information before crossover and mutation, introducing population update operator into traditional CLONALG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of the assistant decision-making model solved by ICLONALG-NN. <\/jats:p>","DOI":"10.1142\/s1793962317500337","type":"journal-article","created":{"date-parts":[[2017,1,26]],"date-time":"2017-01-26T03:13:47Z","timestamp":1485400427000},"page":"1750033","source":"Crossref","is-referenced-by-count":0,"title":["Improved clonal selection algorithm optimizing Neural Network for solving terminal anti-missile collaborative intercepting assistant decision-making model"],"prefix":"10.1142","volume":"08","author":[{"given":"Jinke","family":"Xiao","sequence":"first","affiliation":[{"name":"Air And Missile Defense College, Air Force Engineering University, Xi\u2019an 710051, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Li","sequence":"additional","affiliation":[{"name":"Air And Missile Defense College, Air Force Engineering University, Xi\u2019an 710051, P. R. 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