{'fraction_non_alphanumeric': 0.010238907849829351, 'fraction_numerical': 0.0, 'mean_word_length': 4.98639455782313, '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}}
Study on Ship Traffic Prediction Model with Grey Neural Network

In order to reduce prediction error of ship traffic and improve its prediction accuracy, the Grey neural network model of ship traffic is constructed after the advantages and disadvantages of the conventional Grey model and BP neural network model have been analyzed. The new model gives full play to the characters of low data demand of Grey model and strong nonlinear fitting ability of BP neural network. It uses actual measured data as initial ones to construct different Grey model, various prediction results as inputs of neural network and then the optimized model is obtained. A case study shows that as a better than single forecasting model, the Grey neural network model can offer improved prediction accuracy and ideal prediction result. Therefore, it is feasible and effective to predict ship traffic.