{'fraction_non_alphanumeric': 0.022280471821756225, 'fraction_numerical': 0.000655307994757536, 'mean_word_length': 5.581896551724138, '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 of core muscle strength prescription based on improved PSO-BP neural network

The MKXL-B core muscle strength training system is a set of medical equipment which relieve or cure common low back pain in patients. The prescription design of traditional muscle strength training medical device is usually based on the patient’s physical condition and the expert’s own experience. Due to the patient’s own particularity and other reasons, the expert’s prescription may not be suitable for the training of the patients. The expert will comprehensively evaluate the patient’s condition to design the parameters of the prescription. This process is a waste of time and consumes medical resources. In view of the above problems, this paper presents an optimization algorithm based on improved particle swarm optimization and BP neural network. The error back propagation(BP) algorithm is used to realize the non-linear relationship [1]among prescription parameters. However, the BP algorithm has the disadvantage, which being trapped in local minimum. In order to solve this problem, an improved particle swarm algorithm(PSO) is proposed, which give the global optimal core muscle strength training prescription output parameters, namely compaction pressure and relaxation pressure. Through simulation and optimization experiments on the prescription parameters of different patients, verifying the accuracy and effectiveness of the algorithm, at the same time, the required accuracy requirements are basically satisfied.