CAJ | 학술논문

针对传统近红外光谱波长选择方法忽略模型中非线性因素的缺陷，采用具有非线性处理能力的最小二乘支持向量机，结合间隔策略的波长选择方法和联合区间的思想，提出了一种非线性模型下的波长筛选算法-联合区间最小二乘支持向量机（synergy interval least squares support vector machines ，siLSSVM ）。以苹果糖度近红外光谱数据为例，与传统siPLS波长筛选方法相比，新算法的预测集均方根误差（RMSEP）在PLS模型和LSSVM 模型预测时分别提高了37.43％和47.88％，预测集相关系数（RP）在 PLS 模型和LSSVM模型预测时分别增加了6.04％和7.31％。实例表明，对于存在非线性因素较强的光谱数据，siLSS-VM算法能够有效的挑选最优波长区间与提高模型的预测精度和鲁棒性，为近红外光谱在非线性因素下筛选波长提供了新前景。
침대전통근홍외광보파장선택방법홀략모형중비선성인소적결함，채용구유비선성처리능력적최소이승지지향량궤，결합간격책략적파장선택방법화연합구간적사상，제출료일충비선성모형하적파장사선산법-연합구간최소이승지지향량궤（synergy interval least squares support vector machines ，siLSSVM ）。이평과당도근홍외광보수거위례，여전통siPLS파장사선방법상비，신산법적예측집균방근오차（RMSEP）재PLS모형화LSSVM 모형예측시분별제고료37.43％화47.88％，예측집상관계수（RP）재 PLS 모형화LSSVM모형예측시분별증가료6.04％화7.31％。실례표명，대우존재비선성인소교강적광보수거，siLSS-VM산법능구유효적도선최우파장구간여제고모형적예측정도화로봉성，위근홍외광보재비선성인소하사선파장제공료신전경。
The present paper proposes a wavelength selection algorithm based on nonlinear factors named Synergy interval least squares support vector machines (siLSSVM ) .siLSSVM combines the interval strategy of wavelength selection method with the idea of synergy interval and overcomes the disadvantages of the traditional wavelength selection methods ,i .e .ignoring the non-linear factors .Taking the near infrared spectrum data of apple sugar as performance verification object of this new algorithm , comparing new algorithm with siPLS ,the model performance has been greatly improved .The root-mean-square error (RMSEP) in new algorithm has increased respectively by 37.43% and 47.88% under the model of PLS and LSSVM ,with increases of 6.04% and 7.31% in the correlative coefficient (RP) .The examples illustrate that siLSSVM can efficiently select the optimum wavelength interval for spectrum data with strong nonlinear factors .This algorithm greatly improves the prediction accuracy and robustness of the model ,which provides a new prospect for near infrared spectral with nonlinear factors to select wavelength .