Extreme Learning Machine with a Modified Flower Pollination Algorithm for Filter Design

Authors

  • Li-Ye Xiao School of Physics University of Electronic Science and Technology of China, Chengdu, 610054, China
  • Wei Shao School of Physics University of Electronic Science and Technology of China, Chengdu, 610054, China
  • Sheng-Bing Shi School of Physics University of Electronic Science and Technology of China, Chengdu, 610054, China
  • Zhong-Bing Wang School of Physics University of Electronic Science and Technology of China, Chengdu, 610054, China

Keywords:

Extreme learning machine (ELM), filter design, flower pollination algorithm (FPA), steepest descent method (SDM)

Abstract

In this paper, a modified flower pollination algorithm (FPA) based on the steepest descent method (SDM) is proposed to set the optimal initial weights and thresholds of the extreme learning machine (ELM) for microwave filter design. With the proposed SDMFPA, the model trained by the ELM can achieve higher accuracy with smaller training datasets for electromagnetic modeling, comparing to that achieved by traditional artificial neural network. The validity and efficiency of this proposed method is confirmed by a parametric modeling example of filter design.

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Published

2021-07-25

How to Cite

Li-Ye Xiao, Wei Shao, Sheng-Bing Shi, & Zhong-Bing Wang. (2021). Extreme Learning Machine with a Modified Flower Pollination Algorithm for Filter Design. The Applied Computational Electromagnetics Society Journal (ACES), 33(03), 279–284. Retrieved from https://journals.riverpublishers.com/index.php/ACES/article/view/9211

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