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FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification

FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification

Bighnaraj Naik, Janmenjoy Nayak, H.S. Behera
Copyright: © 2018 |Volume: 5 |Issue: 1 |Pages: 21
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522547013|DOI: 10.4018/IJRSDA.2018010102
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MLA

Naik, Bighnaraj, et al. "FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification." IJRSDA vol.5, no.1 2018: pp.13-33. http://doi.org/10.4018/IJRSDA.2018010102

APA

Naik, B., Nayak, J., & Behera, H. (2018). FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification. International Journal of Rough Sets and Data Analysis (IJRSDA), 5(1), 13-33. http://doi.org/10.4018/IJRSDA.2018010102

Chicago

Naik, Bighnaraj, Janmenjoy Nayak, and H.S. Behera. "FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification," International Journal of Rough Sets and Data Analysis (IJRSDA) 5, no.1: 13-33. http://doi.org/10.4018/IJRSDA.2018010102

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Abstract

Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between exploration and exploitation is one of the important aspects of nature inspired optimizations. In this paper, a recently developed nature inspired algorithm such as black hole algorithm has been used with the functional link neural network for handling the nonlinearity nature of system identification. Specifically, the proposed hybrid approach is used to solve classification problem. The results of the hybrid approach are compared with some of the other popular competent nature based approaches and found the superiority of the proposed method over others. Also, a brief discussion on the working principles of the black hole algorithm and its available literatures are discussed.

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