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

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

Bighnaraj Naik (Veer Surendra Sai University of Technology, Department of Computer Application, Odisha, India), Janmenjoy Nayak (Department of CSE, Sri Sivani College of Engineering, India) and H.S. Behera (Veer Surendra Sai University of Technology, Department of Computer Science Engineering and Information Technology, Odisha, India)
Copyright: © 2018 |Pages: 21
DOI: 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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Introduction

Since last decade, nature inspired optimization algorithms has always been a hot topic of research for the optimization community. In the earlier days of computing, many real life complex problems were solved by using some heat and trail methods. Unfortunately, these methods do not work for solving complex real valued problems. So, researchers step forwarded for the development of some competitive optimization algorithms, which are efficient to solve the complex problems. Till date there has been a no. of optimization techniques developed. Most of such techniques are inspired from either nature or any animal or plant species. However, the applications of population based algorithms have captured a special attention.

According to free lunch theorem (Wolpert,1997), there are no such algorithm which is able to solve all type of problem and is relatively better than any of the other competitive algorithm. For any real world single objective problem, the optimal solution is clearly defined, which does not hold for multiobjective problems. The 90’s decade has seen a golden era of using evolutionary techniques such as Genetic Algorithm, Differential Evolution etc. Evolutionary techniques are the stochastic methods, which are based on the simulation of evolution process of nature. As they are able to capture the multiple Pareto-optimal solutions in a distinct simulation run, evolutionary techniques are well suited to solve multiobjective problems as compared to other blind search methods (Fonseca et. al., 1995; Fonseca et. al., 1998; Valenzuela-Rend´on & Uresti-Charre,1997). At some saturation point of view, few researches were against the use of evolutionary techniques due to some facts. As evolutionary algorithms involve a strict fitness computation, which is very complex to calculate, time consuming and affects the computational complexity. Solving certain optimization problems like variant problems is difficult to solve through evolutionary algorithms, due to the poor fitness function and production of bad chromosomes. Another aspect of these algorithms is, if the whole population is in an improved stage, it does not indicate for the improvement of a single solution within that population. Moreover, there is no fixed response time for these algorithms i.e. the gap between the shortest and longest optimization response time is too large. Also, these algorithms do not guarantee for reaching at global optima.

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