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Modified Adaptive Neuro-Fuzzy Inference System Trained by Scoutless Artificial Bee Colony

  • Mohd Najib Mohd Salleh
  • Norlida Hassan
  • Kashif Hussain
  • Noreen Talpur
  • Shi Cheng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

Neuro-fuzzy systems have produced high accuracy in modeling numerous real-world applications. However, the in-built computational complexity and curse of dimensionality often cease opportunities of implementations in applications with large input size. This is also true with adaptive neuro-fuzzy inference system (ANFIS) as mostly the applications in literature are with small input size. The five-layer architecture of ANFIS is modified in this paper to reduce computational cost. For effective parameters training, the popular swarm-based metaheuristic algorithm Artificial Bee Colony (ABC) algorithm is employed after modification for enhanced convergence ability. The proposed ABC variant eliminates scout bees, hence called ABC-Scoutless, outperforms standard ABC and particle swarm optimization (PSO) on benchmark test functions. The modified ANFIS trained by ABC-Scoutless performs equally better as standard ANFIS on benchmark classification problems with different input range, but with less computational cost due to reduced number of trainable parameters.

Keywords

Neuro-fuzzy Adaptive neuro-fuzzy inference system (ANFIS) Artificial bee colony Metaheuristic Classification 

Notes

Acknowledgment

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research under Postgraduate Incentive Research Grant, Vote No. U560.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohd Najib Mohd Salleh
    • 1
  • Norlida Hassan
    • 1
  • Kashif Hussain
    • 1
  • Noreen Talpur
    • 1
  • Shi Cheng
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anChina

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