近红外光谱药品鉴别作为识别假冒伪劣药品的一种有效技术手段, 已被广泛应用到各大医疗行业和药品监督管理机构, 并结合模式识别建模方法在基层药品打假中得到较好的推广。 由于传统建模方法很难满足药品鉴别中大规模、 多分类、 快速建模等问题, 因此采用一种基于波形叠加极限学习机(SWELM(CS))分类方法对光谱数据进行鉴别。 通过选用极限学习机(ELM)作为光谱药品分类器, 使得分类模型具有快速学习能力以及对训练样本不敏感的特点; 由于极限学习机的连接权值和隐层神经元阈值是随机生成导致网络稳定性差, 因此结合布谷鸟搜索算法优化分类模型参数; 采用反双曲线正弦函数与Morlet小波函数叠加的激励函数代替ELM原有的单一激励函数改善了分类模型的收敛速度和稳健性。 通过上述改进方法使得SWELM(CS)具有对训练样本不敏感性, 布谷鸟参数优化的分类稳定性、 波形叠加函数的强收敛性与信号特征提取能力。 该方法为核函数提供的信号特征提取及拟合的思想, 可推广到其他学习算法中以获取更高的分类准确度及稳定性。 该实验选定西安杨森制药厂生产的249个近红外光谱药品样本作为研究的主要对象, 重点研究光谱药品的二分类和多分类实验, 实验证明SWELM(CS)分类器相比BP神经网络、 标准ELM以及粒子群优化ELM等传统分类器算法具有更高的分类准确度、 分类稳定性及更小的训练样本敏感性。

As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM’s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near infrared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper. Experiments for binary classification and multi-label classification were conducted, and the conclusion proved that the proposed method has more stable performance, higher classification accuracy and lower sensitivity to training samples than the existing ones, such as the BP neural network, ELM and ELM by particle swarm optimization.