{'fraction_non_alphanumeric': 0.022179363548698167, 'fraction_numerical': 0.0, 'mean_word_length': 5.528301886792453, '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}}
Application of Neural Network Based on PSO Algorithm in Prediction Model for Dissolved Oxygen in Fishpond

Based on the study of affected factors for dissolved oxygen (DO) concentration in fishpond, a fuzzy neural network prediction model for DO in fishpond was proposed utilizing the nice approximation ability of fuzzy neural network to given nonlinear systems. Neural networks (NNs) have become one of idea tools in modeling nonlinear relationship between inputs and desired outputs. However, the training of NNs by conventional back-propagation method, i.e. the BP-NNs, has intrinsic vulnerable weakness in slow convergence and local minima. In this work, particle swarm optimization (PSO) algorithm was proposed to train NNs, showing faster convergence rate. The experimental results show that the proposed method is effective and more accurate than BP-NNs and the real-world application is potential. The method proposed establishes foundation for developing intelligent measuring instrument and applying industrialized mariculture