A miniaturized filter design approach using GMDH neural networks

Abstract

A miniaturized microstrip low‐pass filter (LPF) has been designed and fabricated using semicircular‐shaped resonators. The designing procedures as well as the LC equivalent circuit of each step have been reported. Due to the small size of the presented filter, flat group delay in the pass‐band, low insertion loss (IL), and high return loss (RL) in operational frequency. By producing adjusted transmission zeros (TZs), the second to fourth unwanted harmonics were attenuated using the proposed LPF. The machine learning approach has been utilized to adjustment of the filter parameters. The 3 dB cut‐off frequency (Fc), TZs, IL, and RL were predicted using the group method of data handling neural network (GMDHNN). Ninety simulations were performed with different inductor and capacitor values. The collected data were used for neural network training. The GMDHNN can diagnose the efficient inputs, so this feature was applied to determine the efficient L and C elements on TZs, Fc, IL, and RL. Finally, the fabricated filter results are well‐matched with the simulation ones. IL and RL of the suggested filter in operational frequency are −0.09 and −17.26 dB, respectively. The total filter size is only 14.65 mm × 3.5 mm.

Combination of a Nondestructive Testing Method with Artificial Neural Network for Determining Thickness of Aluminum Sheets Regardless of Alloy’s Type

“…Researchers in a wide variety of disciplines, including electronics [22], oil and petrochemicals [23,24], agricultural engineering [25,26], etc., have been interested in neural networks due to their potential as useful tools. In the mentioned research, different neural networks, different feature extraction methods, and different machine learning algorithms have been used in different fields, which can inspire further research.…”