Intelligent fuzzy inference method for generating UUV hull parameters

This study proposes a strategy algorithm based on the fuzzy inference method for the rapid optimization of unmanned underwater vehicle (UUV) hull design parameters.

The initial solutions generated by the genetic algorithm are first fuzzified during the fuzzification stage, 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 calculated UUV solutions. Next, a least learning machine (LLM) 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.

The experimental results show that within 20 seconds, the intelligent fuzzy inference method can infer multiple UUV hull parameter schemes with a combined evaluation membership degree score for speed and range of over 170 points based on the initial UUV hull parameters generated by genetic algorithms.

The proposed method effectively enhances design efficiency and balances speed and range. The findings of this study can provide valuable references for the intelligent and rapid generation of UUV hull parameters.