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Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm

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  • Peilin Liu
  • Wenhao Leng
  • Wei Fang

Abstract

This paper proposes a novel method of training the parameters of adaptive-network-based fuzzy inference system (ANFIS). Different from the previous works which emphasized on gradient descent (GD) method, we present an approach to train the parameters of ANFIS by using an improved version of quantum-behaved particle swarm optimization (QPSO). This novel variant of QPSO employs an adaptive dynamical controlling method for the contraction-expansion (CE) coefficient which is the most influential algorithmic parameter for the performance of the QPSO algorithm. The ANFIS trained by the proposed QPSO with adaptive dynamical CE coefficient (QPSO-ADCEC) is applied to five example systems. The simulation results show that the ANFIS-QPSO-ADCEC method performs much better than the original ANFIS, ANFIS-PSO, and ANFIS-QPSO methods.

Suggested Citation

  • Peilin Liu & Wenhao Leng & Wei Fang, 2013. "Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, June.
    • Handle: RePEc:hin:jnlmpe:595639
    DOI: 10.1155/2013/595639
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    1. Mahdis sadat Jalaee & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Naeeme Amani Zarin & Reza Derakhshani, 2022. "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(7), pages 1-14, April.

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