{'fraction_non_alphanumeric': 0.01699029126213592, 'fraction_numerical': 0.012135922330097087, 'mean_word_length': 5.311224489795919, '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}}
Optimization for Satellite Navigation System Level Parameter Modelling Based on Artificial Neural Network

BP algorithm is a common method to correct satellite navigation system level parameter modelling, which can significantly improve the accuracy of the measurement results. However, the traditional BP algorithm is easy to fall into local minimum, which makes the accuracy and stability of the measurement results poor. In this paper, an optimization algorithm based on immune algorithm and BP neural network is proposed. The global search is carried out by using the immune algorithm, and then the BP algorithm is used for local search. The experimental results show that the optimization algorithm can effectively avoid the traditional BP algorithm falling into local minimum when training multi-layer forward neural network, and has the merits of high prediction precision, rapid convergence, global superiority and accuracy for optimization. The measurement accuracy is improved. Moreover, the mean square error between the satellite navigation system height fitting error and the standard value of the neural network is less than 0.0422, the average error is less than 0.0466 and the maximum relative error is less than 0.0496.