Extreme Learning Machine with a Modified Flower Pollination Algorithm for Filter Design
Li‐Ye Xiao, Wei Shao, Sheng-Bing Shi, Zhongbing Wang · The Applied Computational Electromagnetics Society Journal (ACES) · 2018
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.