{'fraction_non_alphanumeric': 0.017587939698492462, 'fraction_numerical': 0.0, 'mean_word_length': 5.586776859504132, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A frequency response model updating method based on unidirectional convolutional neural network

Abstract A unidirectional convolutional neural network (UCNN)-based approach is proposed in this paper to take advantage of frequency response (FR) data for finite element model updating. UCNN is designed to acquire the high-precision inverse mapping from the FR data to the updating parameters without any artificial feature extraction. Unidirectional convolution is applied at the frequency and location dimensions of the FR data separately to avoid data coupling. UCNN outperforms the residual-based model updating method and two-dimensional convolutional neural networks on a satellite model updating experiment. It achieves highly- precision results both inside and outside of the training set.