Research on the Method for Generating UUV Hull Parameters Based on Intelligent Fuzzy Inference

Abstract：Objectives This study aims to propose a strategy algorithm based on fuzzy inference for the rapid optimization of UUV hull design parameters.. Methods Using fuzzy inference methods, the initial solutions generated by the genetic algorithm are first fuzzified during the fuzzification stage. These initial solutions are then used as training samples, and the antecedent parameters of the fuzzy rules are obtained using an equal interval fuzzy partition strategy, with the membership values of all UUV solutions calculated. Next, the Extreme Learning Machine (ELM) is employed to solve the consequent parameters of the fuzzy rules. Based on the generated antecedent and consequent parameters, new UUV solutions are created, and the evaluation membership values for speed and range are calculated. Finally, these new UUV solutions are tested against the constraint conditions to obtain optimized and compliant UUV design parameters. Results The experimental results show that the intelligent fuzzy inference method can, within 20 seconds, based on the initial UUV hull parameters generated by genetic algorithms, quickly infer multiple UUV hull parameter schemes with a combined evaluation membership degree score for speed and range of over 170 points.Conclusions The method proposed in this study for generating UUV hull parameters based on intelligent fuzzy inference effectively enhances design efficiency and balances speed and range. The research findings can provide beneficial references for the intelligent and rapid generation of UUV hull parameters