Extreme Learning Machine with a Modified Flower Pollination Algorithm for Filter Design
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.
Downloads
References
J. E. Rayas-Sánchez, “EM-based optimization of microwave circuits using artificial neural networks: The state-of-the-art,” IEEE Trans. Microw. Theory Techn., vol. 52, no. 1, pp. 420-435, Jan. 2004.
V. Rizzoli, A. Costanzo, D. Masotti, A. Lipparini, and F. Mastri, “Computer-aided optimization of nonlinear microwave circuits with the aid of electromagnetic simulation,” IEEE Trans. Microw. Theory Techn., vol. 52, no. 1, pp. 362-377, Jan. 2004.
X. Ding, V. K. Devabhaktuni, B. Chattaraj, M. C. E. Yagoub, M. Deo, J. Xu, and Q. J. Zhang, “Neural-network approaches to electromagnetic based modeling of passive components and their applications to highfrequency and high-speed nonlinear circuit optimization,” IEEE Trans. Microw. Theory Techn., vol. 52, no. 1, pp. 436-449, Jan. 2004.
Q. J. Zhang, K. C. Gupta, and V. K. Devabhaktuni, “Artificial neural networks for RF and microwave design—From theory to practice,” IEEE Trans. Microw. Theory Techn., vol. 51, no. 4, pp. 1339- 1350, Apr. 2003.
Y. F. Yam, T. W. S. Chow, and C. T. Leung, “A new method in determining initial weights of feed forward neural networks for training enhancement,” Neurocomputing, vol. 16, pp. 23-32, 1997.
G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
F. Feng, C. Zhang, J. Ma, and Q. J. Zhang, “Parametric modeling of EM behavior of microwave components using combined neural networks and pole-residue-based transfer functions,” IEEE Trans. Microw Theory Techn., vol. 64, no. 1, pp. 60-77, Jan. 2016.
X. S. Yang, “Flower pollination algorithm for global optimization,” in Proc. of the 11th Int'l. Conf. on Unconventional Computation and Natural Computation (UCNC), Orléans, France, pp. 242- 243, Sept. 2012.
E. Nabil, “A modified flower pollination algorithm for global optimization,” Expert. Syst. Appl., vol. 57, pp. 192-203, 2016.
G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
X. Shi, J. Wang, G. Liu, L. Yang, X. Ge, and S. Jiang, “Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs,” J. Nat. Gas. Sci. Eng., vol. 33, pp. 687-702, 2016.
S. R. Schmidt and R. G. Launsby, “Understanding Industrial Designed Experiments,” Colorado Springs, CO, USA, Air Force Academy, 1992.
S. W. Wong, S. F. Feng, L. Zhu, and Q. X. Chu, “Triple- and quadruple-mode wideband bandpass filter using simple perturbation in single metal cavity,” IEEE Trans. Microw Theory Techn., vol. 63, no. 10, pp. 1-9, Oct. 2015.
