{'fraction_non_alphanumeric': 0.019762845849802372, 'fraction_numerical': 0.0031620553359683794, 'mean_word_length': 5.236453201970443, '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}}
Force identification of dynamic systems using genetic programming

One obvious limitation of the traditional force identification techniques is that they are unable to obtain the explicit expression of the force. Moreover, some techniques need both the displacement and velocity data of all freedoms, and some need the Markov parameters from numerical calculation or experimental test before the force identification can be carried out. This paper presents a genetic programming (GP) based method for excitation force identification of dynamic systems to overcome these traditional methods' disadvantages. GP is employed as a search and optimization method to obtain the optimal, if not the best, force expression from the known dynamic response. One obvious merit of the proposed method is that it can obtain the explicit expression of the unknown force. Another advantage is that it only needs the dynamic response data at one point, i.e. displacement or velocity or acceleration of one freedom. Illustrative examples demonstrate that the GP based method is able to identify the excitation force of a single‐degree, a three‐degree dynamic systems and a frame structure, depicting its potential for force forecast problems. Copyright © 2005 John Wiley & Sons, Ltd.