Surrogate‐based model using auto‐encoder for optimising multi‐band antennas

Abstract

Abstract This paper suggests an optimisation method to design multi‐band antenna using artificial neural networks. The proposed network, surrogate‐based model using auto‐encoder (SBM‐AE), is composed of two parts, ordinary neural network and auto‐encoder. First, the front neural network predicts the encoded antenna characteristics and then decodes the predicted data to obtain the antenna characteristics. After training the encoder to obtain a characteristic signature vector, the front neural network regresses only the characteristic signature vectors, reducing the complexity and number of parameters of the neural network. This not only reduces the training time but also significantly reduces the number of training data required. We confirm the effectiveness of the design method by designing a multi‐band antenna where the proposed SBM‐AE required 270 training data while the conventional neural network without auto‐encoder needed 1350 training data to achieve comparatively the same error rate.

Keywords

• auto‐encoder
• broadband antennas
• neural nets
• optimisation
• surrogate based model