volume | PIER Journals

Abstract

E®ective complex permittivity measurements of materials are important in microwave engineering and microwave chemistry. Artificial neural network (ANN) computational module has been used in microwave technology and becomes a useful tool recently. A neural network can be trained to learn the behavior of an effective permittivity of material under microwave irradiation in a test system, and it can provide a fast and accurate result for the permittivity measurement of material. Thus, an on-line measurement has been realized. This paper presents a simple and convenient reconstruction algorithm for determining the dielectric properties of materials. First, a measurement system is designed, and the reflection coefficient is calculated by employing full-wave simulations. Second, an artificial nerve network has been applied, and adequate simulated materials are utilized to train the networks. Last, the trained network is employed to reconstruct the effective permittivity of several organic solvents using the measured scattering parameters, and the reconstructed results for several organic solvents agree well with reference data and the relative errors between them are less than 5%.

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Dr. Qian Chen

Professor Kama Huang

Professor Xiaoqing Yang

Dr. Ming Luo

Dr. Huacheng Zhu