An accurate wavelet neural-network-based model for electromagnetic optimization of microwave circuits

Abstract

In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS (Broyden‐Fletcher‐Goldfarb‐Shanno) and LBFGS (limited memory BFGS) algorithms and their application to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialization for the neural network, and thus improved convergence of the learning algorithm. The optimization method combines a rigorous and accurate global EM analysis of device performed with a finite‐element method (FEM) and a fast neural model deduced from its segmented EM analysis. Finally, experimental results, which confirm the validity of the WNN model, and good agreement between theoretical optimization results and experimental ones are reported. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 297–306, 1999.

Application of Artificial Neural Network (ANN) and Partial Least-Squares Regression (PLSR) to Predict the Changes of Anthocyanins, Ascorbic Acid, Total Phenols, Flavonoids, and Antioxidant Activity during Storage of Red Bayberry Juice Based on Fractal Analysis and Red, Green, and Blue (RGB) Intensity Values

“…Fractal parameters were determined using fractal image analysis software (HarFA, Harmonic and Fractal Image Analyzer 5.4, freeware at http://www.fch.vutbr.cz/lectures/imagesci/) as described by Pandolfi et al (38). The basic procedures were as follows: (i) each fruit color image was split in RGB color channels; (ii) each channel was (49), is a nonlinear mathematical model that has the capability of developing meaningful relationships between input and output variables through a learning process. Many theoretical works have shown that a single hidden layer is sufficient for ANN to approximate any complex nonlinear function (50).…”

“…ANN, the structure and function of which are inspired by the organization and function of the human brain , is a nonlinear mathematical model that has the capability of developing meaningful relationships between input and output variables through a learning process. Many theoretical works have shown that a single hidden layer is sufficient for ANN to approximate any complex nonlinear function .…”

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

“…The RBF-neural network (RBF-NN) is more effective in representing various localized behaviors in the input-output relationship [126]. When the behavior of the problem exhibits high nonlinear phenomena or contains sharp variations, wavelet neural networks (WNNs) can be used, since the localized nature of their hidden neurons makes it easier to train and obtain a promising model accuracy [15], [21], [24]. As a single-hidden layer FFNN, the extreme learning machine (ELM) is found to have a fast learning speed and good performance in EM parametric modeling when the training dataset is not too large [127], [128], [129].…”

Multi-scale benchtop 1H NMR spectroscopy for milk analysis

“…Artificial Neural Networks (ANN) model is a nonlinear mathematical model with the capability of developing meaningful relationships between input and output variables through a learning process (Zheng et al, 2011). The function and organization of the human brain gave inspiration to its function and structure (Bila et al, 1999). The mathematical model designed to classify milk samples with respect to their fat and sugar contents, milk source, production and processing type was based on nonlinear ANNs.…”