基于多策略-交通拥堵优化-支持向量回归算法的重组竹参数及质量的双向预测OA

In the hot-press molding process of reconstituted bamboo,the coupling of multiple process parameters,reli-ance on empirical parameter adjustment,and high trial-and-error costs make it necessary to establish a quantitative map-ping relationship between process parameters and quality indicators,and further realize reverse prediction of parameters for target performance.To address this issue,a support vector regression(SVR)method optimized by a multi-strategy(MS)traffic jam optimization(TJO)algorithm,namely MS-TJO-SVR,is proposed to develop a bidirectional prediction model for the process parameters and quality indicators of reconstituted bamboo.In the forward prediction,density,moisture content,adhesive content,and pressure holding time are used as input process parameters,while modulus of rupture,horizontal shear strength,water absorption width swelling rate,and water absorption thickness swelling rate are used as output quality indicators.In the reverse prediction,the quality indicators are used as inputs to predict the corre-sponding process parameters.By jointly optimizing the key hyperparameters of SVR,MS-TJO enhances the model's abil-ity to characterize nonlinear relationships and improves prediction stability.The results indicate that MS-TJO-SVR achieves high fitting accuracy and low prediction error in both forward and reverse prediction tasks,and outperforms tra-ditional SVR and other optimized SVR methods in overall performance.This study provides an effective modeling tool and methodological reference for process parameter optimization and quality prediction in the hot-press molding of recon-stituted bamboo.