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Model-Free Adaptive Iterative Learning Control Based on Data-Driven for Noncircular Turning Tool Feed System Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Improved Clonal Selection Algorithm Optimizing Neural Network for Solving Terminal Anti-missile Collaborative Intercepting Assistant Decision-Making Model

Authors : Jin-ke Xiao, Wei-min Li, Xin-rong Xiao, Cheng-zhong Lv

Published in: Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems

Publisher: Springer Singapore

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Abstract

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is one key problem to enhance operational efficiency. 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 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 assistant decision-making model solved by ICLONALG-NN.

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Metadata
Title
Improved Clonal Selection Algorithm Optimizing Neural Network for Solving Terminal Anti-missile Collaborative Intercepting Assistant Decision-Making Model
Authors
Jin-ke Xiao
Wei-min Li
Xin-rong Xiao
Cheng-zhong Lv
Copyright Year
2016
Publisher
Springer Singapore
Book
Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems

Print ISBN: 978-981-10-2665-2

Electronic ISBN: 978-981-10-2666-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-2666-9_22

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