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International Conference on Hybrid Intelligent Systems

HIS 2017: Hybrid Intelligent Systems pp 204–212Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 734))

Abstract

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.

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Acknowledgement

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).

Author information

Authors and Affiliations

  1. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik & Debasmita Mishra

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, 535402, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Communication Sciences, University of Teramo, Coste Sant’agostino Campus, Teramo, Italy

    Danilo Pelusi

  4. Machine Intelligence Research Labs (MIR Labs), Washington, USA

    Ajith Abraham

Authors
  1. Bighnaraj Naik
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  2. Debasmita Mishra
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  3. Janmenjoy Nayak
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  4. Danilo Pelusi
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  5. Ajith Abraham
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Corresponding author

Correspondence to Debasmita Mishra .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Naik, B., Mishra, D., Nayak, J., Pelusi, D., Abraham, A. (2018). Perturbation Based Efficient Crow Search Optimized FLANN for System Identification: A Novel Approach. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-76351-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76350-7

  • Online ISBN: 978-3-319-76351-4

  • eBook Packages: EngineeringEngineering (R0)

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