Perturbation Based Efficient Crow Search Optimized FLANN for System Identification: A Novel Approach

  • Bighnaraj Naik
  • Debasmita MishraEmail author
  • Janmenjoy Nayak
  • Danilo Pelusi
  • Ajith Abraham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)


Designing an efficient classifier is a tough task as it must be suitable for solving maximum real life problems with high accuracy and less error rate. In this paper, a novel functional link neural network based system identification model is developed to solve the classification problem of data mining. To increase the accuracy of the model and for an optimized performance, an enhanced crow search algorithm (CSA) with perturbation has been introduced. This enhanced version of CSA based model avoids premature convergence and stagnation in classical CSA, by introducing the new neighbourhood searching operation through perturbation. Experimental results reveal that the proposed model outperforms several other standard models in terms of accuracy and error rate.


Crow search algorithm Perturbation FLANN Classification Neural network 



This work is supported by Technical Education Quality Improvement Programme, National Project Implementation Unit (A unit of MHRD, Govt. of India, for implementation of World Bank assisted projects in technical education), under the research project grant (VSSUT/TEQIP/37/2016).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bighnaraj Naik
    • 1
  • Debasmita Mishra
    • 1
    Email author
  • Janmenjoy Nayak
    • 2
  • Danilo Pelusi
    • 3
  • Ajith Abraham
    • 4
  1. 1.Department of Computer ApplicationVeer Surendra Sai University of TechnologyBurla, SambalpurIndia
  2. 2.Department of Computer Science and EngineeringSri Sivani College of EngineeringSrikakulamIndia
  3. 3.Communication SciencesUniversity of TeramoTeramoItaly
  4. 4.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA

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