一类基于模糊神经元的复杂非线性化学模式识别方法

针对模式类别边界曲折而模糊的复杂化学模式分类问题,提出一种化学模式模糊分类方法,并给出其模糊神经元分类器设计和网络训练算法,使模糊神经元分类器具有学习功能.以一个应用实例检验了该方法的实效.     

天然植物复杂化学模式特征的分步提取法

在运用神经元计算技术对高维小样本复杂化学模式进行分类时,通过模式特征提取,降低输入变量维数,能使复杂的模式分类问题比较容易解决。根据模式类别相关分步分析思路,提出复杂化学模式特征分步提取法,可将原始模式数据中与类别指标相关较大的特征量有效地提取出来。应用于天然植物组效关系辨识结果表明,这种化学模式特征提取方法比经典主成分分析法更为实用可靠。     

基于RF_AdaBoost模型的血液种属鉴别算法

针对人类和非人类血液种属鉴别对无损、 高效分析方法的需求, 结合随机森林(Random Forest)和AdaBoost(Adaptive Boosting Algorithm)算法, 提出了一种血液种属鉴别方法(RF_AdaBoost). 该方法将RF作为AdaBoost的弱分类器, 以达到提高模型鉴别准确度, 增强模型鲁棒性的目的. 采用RF、 支持向量机(SVM)、 极限学习机(ELM)、 核极限学习机(KELM)、 堆栈自编码网络(SAE)、 反向传播网络(BP)、 主成分分析-线性判别法(PCA-LDA)及偏最小二乘判别分析(PLS-DA)与RF_AdaBoost模型进行对比, 以不同规模血液拉曼光谱数据训练集进行鉴别实验评估其性能. 结果表明, 随着训练样本的增加, RF_AdaBoost鉴别准确度最高达100%, 预测标准偏差趋于0. 与其它模型相比, RF_AdaBoost具有较高的分类准确度及较强的稳定性, 为血液种属的鉴别工作提供了新方法.     

基于Person Ⅶ核函数的支持向量机及其在化学模式分类中的应用

化学模式分类问题通常是非线性的,而且比较复杂,难以用经典统计方法建立分类判别模型。以支持向量机(SVM)构建的分类器具有更好的分类性能。对于非线性分类,SVM通过核函数将其映射到高维特征空间中,然后再进行线性分类。因此,核函数往往是决定SVM非线性分类性能的关键。实际应用时,一般通过选择几种核函数,并对其参数进行优化,然后根据分类器的预测性能来决定,训练过程非常耗时,而且结果难以保证最优。为此,采用一种通用性的核函数,即PersonⅦ核函数(PUKF),它可取代目前常用的几种核函数,可避免SVM非线性分类器训练过程的核函数选择问题。本研究将基于PUKF的SVM分类器应用于两个化学模式分类问题,均取得了较好的结果。对于多类分类,设计了一种子分类器的构造方法,它在分类性能保持较好的情况下,简化了多类分类器结构,大大降低了计算量。     

用于药品质量快速检测的近红外光谱模糊神经元分类方法

针对非线性且分类界线模糊的药品质量类别快速测定难题, 将近红外光谱分析与模糊神经网络相结合, 经研究提出近红外光谱模糊神经网络分类方法, 用于计算辨析中药等化学组成复杂药品的近红外光谱模式类别, 从而快速评定这类药品的质量. 以参麦注射液为典型分析对象, 以鉴别其生产厂家这一模式分类问题为例, 考核本文方法, 结果表明, 其分类准确率达到94.2%, 明显优于经典的BP神经网络分类方法(84.6%), 可望用于中药产品质量类别的快速检测与评价.     

用于药品质量快速检测的近红外光谱模糊神经元分类方法

针对非线性且分类界线模糊的药品质量类别快速测定难题, 将近红外光谱分析与模糊神经网络相结合, 经研究提出近红外光谱模糊神经网络分类方法, 用于计算辨析中药等化学组成复杂药品的近红外光谱模式类别, 从而快速评定这类药品的质量. 以参麦注射液为典型分析对象, 以鉴别其生产厂家这一模式分类问题为例, 考核本文方法, 结果表明, 其分类准确率达到94.2%, 明显优于经典的BP神经网络分类方法(84.6%), 可望用于中药产品质量类别的快速检测与评价.     

人工神经网络-紫外分光光度法同时测定苯酚、苯胺和苯甲酸

采用人工神经网络方法和紫外分光光度法相结合研究多组分的同时测定,系统地研究了神经网络方法对苯酚、苯甲酸和苯胺混合样品同时测定的可行性.在大量实验数据的基础上采用matlab语言编写两层BP神经网络,并初步探讨了网络参数对网络预测性能的影响.结果表明,BP神经网络能在不改变样品性质的前提下实现了多组分的同时测定,最大限度地保护了样品不被破坏.方法的回收率为96.9%～109.3%,RSD为0.90%～1.28%(n=5).通过时不同隐层神经元数目的研究发现,当隐层神经元数目达到100时,仿真结果和实验数据相似程度达到了90%以上,且随隐层神经元数目增多,训练误差下降速度加快,但训练速度变慢,训练时间延长.     

粒子群最小二乘支持向量机结合偏最小二乘法用于芝麻油质量的鉴别

结合粒子群最小二乘支持向量机(PSO-LSSVM)与偏最小二乘法(PLS)提出一种基于气相色谱技术的新方法,对芝麻油进行真伪鉴别,并对掺伪品中掺假比例进行定量分析。采用主成分分析法(PCA)对857个样本的脂肪酸色谱数据进行分析,优选主成分作为最小二乘支持向量机(LSSVM)的输入向量。利用粒子群算法(PSO)优化LSSVM,构建芝麻油掺伪鉴别的两级分类模型,同时运用PLS建立掺伪芝麻油中掺伪油脂的定量校正模型,两级分类模型的准确率分别达到了100%和98.7%,定量分析模型的平均预测标准偏差(RMSEP)为3.91%。结果表明,本方法的鉴别准确性和模型泛化能力均优于经典的BP神经网络和支持向量机(SVM),可用于食用油脂加工和流通环节的质量控制,为食用油质量的准确鉴定提供了一条有效途径。     

一种模式分类降维策略及其在复杂化学信息处理中的应用

采用分类相关分析方法处理复杂化学信息的模式分类问题。从高维模式中提取的分类相关成分,它们相互独立,并集中了原有模式的分类信息。提出由相关量份额选择分类相关成分,计算简便,用以替代原模式,可以显著地降维,使问题简化,分类效果良好。该方法应用于天然留兰香油多类判别,结果良好。     

柑橘黄龙病检测的近红外光谱集成建模方法

针对黄龙病检测问题,提出了一种集成了多特征提取模型和多分类器的柑橘黄龙病检测算法。将谱回归核判别分析和主成分分析并行融合进行特征提取,将偏最小二乘判别分析、决策树和支持向量机利用Stacking策略融合完成分类任务。基于3个主要柑橘品种共1 620条近红外光谱数据,与单特征提取单分类器方法和多特征提取单分类器方法进行对比,集成分类模型的正确率可达98.52%,精度在98.57%以上,F2得分可达98.01%。实验结果表明,集成分类模型明显优于单特征提取单分类模型和多特征提取单分类模型,证明利用集成分类模型进行柑橘黄龙病的无损检测是可行的,为其他领域的光谱分类提供参考。     

基于核函数的非线性分类相关分析及其在化学模式识别中的应用

与统计分析和神经网络相比,基于结构风险最小的支持向量机有更好的分类性能。它用于非线性分类时,先将样本映射到更高维的特征空间,往往会增加复共线性与冗余信息,将影响样本分布,降低线性支持向量机分类器(LSVC)的预测性能。本研究提出非线性分类相关分析算法(NLCCA),利用核函数技术,无需了解非线性映射的算式,从特征空间的样本映像中提取分类相关成分,以消除冗余信息,改善样本分布。由此构建的NLCCA-LSVC集成分类器具有优良的预测性能。经模拟数据的测试,并实际用于两个复杂的化学模式识别问题,均取得令人满意的效果,也印证了算法的有效性。     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

Identification of epilepsy from intracranial EEG signals by using different neural network models

In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis.     

Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.     

Variable predictive model based classification algorithm for effective separation of protein structural classes

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25-100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.     

神经网络方法用于分辨3种化学物质

神经网络方法用于分辨３种化学物质冯伟,胡上序（浙江大学化工系,杭州,３１００２７）关键词模式分类,神经网络,模拟退火,遗传算法,传感器阵列传感器阵列技术是利用传感器阵列所提供的交叉敏和模式识别及微机处理技术,来提高传感器的选择性和传感器的测量精度［１...