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Theory and Applications of Bioinspired Neural Intelligence for Robotics and Control
Author(s) -
Simon X. Yang,
Chaomin Luo,
Howard Li,
Jianjun Ni,
Jianwei Zhang
Publication year - 2016
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2016/5089767
Subject(s) - flexibility (engineering) , computational intelligence , computer science , artificial intelligence , robotics , function (biology) , parallelism (grammar) , artificial neural network , nonlinear system , space (punctuation) , complex system , control (management) , machine learning , robot , mathematics , statistics , physics , quantum mechanics , evolutionary biology , parallel computing , biology , operating system
Computational intelligence approaches are nature-inspired methods, which offer a wealth of ideas for solutions to complex problems. In comparison to the traditional approaches, the computational intelligence approaches are more powerful so that they do not need the reformulation of the problem to search a nonlinear and a nondifferentiable space with real world conditions with the massive parallelism. Another advantage of the computational intelligence approaches is the flexibility of the fitness function formulation, which can be expressed as a proper function of the system outputs and are suitable for multiobjective problems.

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