Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Abstract

Smart tuning of a filter depends very much on an accurate parametric model. Here, the authors develop a parametric model for a microwave filter based on an improved extreme learning machine (ELM). First, a coupling matrix is extracted from scattering parameters in undesirable states. The automatic decomposition and modular training strategy greatly reduces the network complexity. Next, by increasing the size of the output matrix of the hidden‐layer, the number of hidden layer nodes can be changed. Finally, the model parameters are optimised by combining the particle swarm optimisation (PSO) algorithm and the differential evolution (DE) algorithm. The presented parametric model benefits from the information of the fusion mechanism with a hybrid optimisation algorithm and succeeds in avoiding slow convergence and premature influence on the system. It also achieves the desired training accuracy within the target time. Compared with prediction based on back‐propagation (BP) neural networks and least‐squares support vector machines (LS‐SVMs), the proposed method can be generalised and has a better modelling accuracy.

Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning

“…Similarly, Zhou et al exploited Support Vector Regression (SVR) to construct metamodels between screw rotations and coupling matrix change [6,14]; the screw rotations are not measured but obtained from two optimization procedures, and the calculation of coupling matrix also brings uncertainties. Furthermore, fuzzy logic control [7,15], neural-fuzzy control [16], Extreme Learning Machine (ELM) [17], and regularized deep belief network (R-DBN) [18] have also been applied in modeling cavity filters. These data-driven methods learn models simply from pregathered data without analyzing the complex theoretical and physical characteristics of the filter product, eliminating the error produced by the difference between the ideal model and the real product.…”

Second-Order Sliding Mode Formation Control of Multiple Robots by Extreme Learning Machine

“…Compared with the back-propagation algorithm, the training and learning speed of the ELM is much faster. So far, the ELM has been successfully applied to microwave filters [28], traffic accident detection [29], air-fuel ratio control [30], and so on. However, application of the ELM technique to the formation problem of multi-agent mobile robots has not been reported.…”