用于昆虫分类鉴定的人工神经网络方法研究:主成分分析与数学建模

为探讨人工神经网络(ANN)在昆虫分类上的可行性,本文提出利用主成分分析和数学建模等方法相结合改进ANN,并以鳞翅目夜蛾科6种蛾类昆虫为样本进行了验证.首先利用Bugshape1.0特征提取软件获取6种蛾180个右前翅样本的13项数学形态特征数据,再运用主成分分析对蛾翅数学形态特征变量重新组合生成新的综合变量,然后结合主成分分析建立BP神经网络分类器.主成分分析结果表明,前5个主成分的累积贡献率为85.52%,已基本包含了全部特征变量具有的信息.在主成分分析的基础上,建立具有5个输入层节点,12个隐含层节点和1个输出层节点的三层BP神经网络分类器.每种蛾20个样本共120组特征数据对分类器进行训练和仿真,其余60组特征数据对分类器进行验证,仿真输出值与目标值的相关系数R=0.997,分类正确率达到了93.33%.较之未经过主成分分析而单独使用BP神经网络建立的分类器,基于主成分分析的BP神经网络分类器具有更优的性能和更准确的分类能力.研究结果表明本文提出的方法具有很好的分类和鉴别作用,为蛾种类的鉴别提供了一种可行的方法.     

基于OverFeat模型的长江口南汇潮滩植被分类

潮滩地带环境复杂多变, 有些植被之间光谱特性相似, 为了解决植被精细分类精度不高的问题, 利用基于ImageNet预训练的卷积神经网络OverFeat模型, 以高分二号(GF-2)卫星遥感影像作为实验数据, 对长江口南汇潮滩不同生长状态的植被进行了深度特征提取, 然后将模型训练好的深度特征输入到支持向量机(SVM)分类器中, 得到植被分布信息。研究结果表明, 与基于光谱特征的SVM分类方法相比, 文章所用方法的分类精度更高, 总体精度可达96.08%, 证明了使用基于ImageNet数据集的预训练卷积神经网络对不同生长状态的植被可以实现较好的识别。     

基于优化核参数支持向量机的意识任务分类

根据支持向量机的基本原理,给出一种推广误差上界估计判据,并利用该判据进行最优核参数的自动选取。对三种不同意识任务的脑电信号进行多变量自回归模型参数估计,作为意识任务的特征向量,利用支持向量机进行训练和分类测试。分类结果表明,优化核参数的支持向量机分类器取得了最佳的分类效果,分类正确率明显高于径向基函数神经网络。     

随机森林：一种重要的肿瘤特征基因选择法

特征选择技术已经被广泛地应用于生物信息学科,随机森林（random forests,RF）是其中一种重要的特征选择方法。利用RF对胃癌、结肠癌和肺癌等5组基因表达谱数据进行特征基因选择,将选择结果与支持向量机（support vector machine,SVM）结合对原数据集分类,并对特征基因选择及分类结果进行初步的分析。同时使用微阵列显著性分析（significant analysis of microarray,SAM）和ReliefF法与RF比较,结果显示随机森林选择的特征基因包含更多分类信息,分类准确率更高。结合该方法自身具有的分类方面的诸多优势,随机森林可以作为一种可靠的基因表达谱数据分析手段被广泛使用。     

基于小波和神经网络的动态心电波形分类新方法

利用小波分析提取动态心电波形（DECG）的概貌信息。然后用所得概貌信息作为神经网络的输入,对DECG进行分类。这样一方面可以使神经网络的输入点大大减少,提高了神经网络的分类速度;另一方面也可以看作是对DECG数据的压缩,使数据量大为减少,而其基本的形态特征基本上没有损失,同时还在一定程度上降低了噪声的影响。用MIT数据库中的数据作实验表明所提出的方法简单、易行,分类速度和分类精度都比原有方法提高。     

基于人工神经网络的生态环境质量遥感评价

利用ETM遥感数据提取反映生态环境的植被、土壤亮度、湿度,MODIS地表温度产品提取的热度指数、气象指数及其它地学辅助信息作为神经网络的输入,野外调查标准兴趣区的遥感本底值评分值作为网络输出,建立一个3层结构的BP神经网络生态环境遥感本底值预测模型.利用MATLAB软件对网络进行训练和研究区生态环境遥感本底值的预测输出,并将预测结果按照生态环境遥感本底值分级评分标准进行等级划分.结果表明,总体分类精度达87.8%.利用神经网络方法对生态环境遥感本底值进行预测是可行的.采用先预测再分级的方法不仅能很好地评价区域生态环境质量,而且能够和区域生态环境类型紧密的结合起来.     

PCA 和PLS 应用于胃癌亚型分类研究

文章研究了基于微阵列基因表达数据的胃癌亚型分类。微阵列基因表达数据样本少、纬度高、噪声大的特点,使得数据降维成为分类成功的关键。作者将主成分分析(PCA) 和偏最小二乘（PLS）两种降维方法应用于胃癌亚型分类研究,以支持向量机（SVM）、K- 近邻法（KNN）为分类器对两套胃癌数据进行亚型分类。分类效果相比传统的医理诊断略高,最高准确率可达100%。研究结果表明,主成分分析和偏最小二乘方法能够有效地提取分类特征信息,并能在保持较高的分类准确率的前提下大幅度地降低基因表达数据的维数。     

基于CNN与LSTM模型的蛋白质二级结构预测

蛋白质结构的预测在理解蛋白质结构组成和蛋白质的生物学功能有重要意义,而蛋白质二级结构预测是蛋白质结构预测的重要环节。当PSSM位置特异性进化矩阵被广泛应用于将蛋白质初级结构序列编码作为输入样本后,每个残基可以被表示成二维空间的数据平面,由此文中尝试利用卷积神经网络对其进行训练。文中还设计了另一种卷积神经网络,利用长短记忆网络感知了CNN最后卷积特征面的横向特征和纵向特征后连同卷积神经网络的全连接共同完成分类,最后用ensemble方法对两类卷积神经网络模型进行了整合,最终ensemble方法中包含两类卷积神经网络的六个模型,在CB513蛋白质数据集测得的Q3结果为77.2。     

基于小波神经网络的麦蚜发生程度预测模型

【目的】建立基于小波神经网络病虫害预测预报模型,对提前采取防病防虫措施、减少农作物病虫害损失、提高农作物产量与质量具有重要意义。【方法】本研究以山西省运城市芮城县1980-2014年麦蚜发生程度和气象因子数据为基础,采用主成分分析法从40个基础气象因子中整合形成9个新的自变量输入模型,采用试凑法筛选隐含层节点数,用1980-2009年的数据进行网络训练,对2010-2014年麦蚜发生程度进行回测,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较。【结果】小波和BP神经网络两种模型对训练样本的平均拟合精度均有10年以上超过80%,两者MAPE 值分别为 89.83% 和83.07%,MSE 值分别为0.0578和0.6192。【结论】两个模型都能较好地描述麦蚜发生程度;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络。     

基于模拟退火算法的高分辨率蛋白质质谱数据特征选择

蛋白质质谱技术是蛋白质组学的重要研究工具,它被出色地应用于癌症早期诊断等领域,但是蛋白质质谱数据带来的维灾难问题使得降维成为质谱分析的必需的步骤。本文首先将美国国家癌症研究所提供的高分辨率SELDI—TOF卵巢质谱数据进行预处理;然后将质谱数据的特征选择问题转化成基于模拟退火算法的组合优化模型,用基于线性判别式分析的分类错误率和样本后验概率构造待优化目标函数,用基于均匀分布和控制参数的方法构造新解产生器,在退火过程中添加记忆功能;然后用10-fold交叉验证法选择训练和测试样本,用线性判别式分析分类器评价降维后的质谱数据。实验证明,用模拟退火算法选择6个以上特征时,能够将高分辨率SELDI—TOF卵巢质谱数据全部正确分类,说明模拟退火算法可以很好地应用于蛋白质质谱数据的特征选择。     

Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis

MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer.     

Weak electric fields detectability in a noisy neural network

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.     

The application of an artificial neural network in the identification of medicinal rhubarbs by near-infrared spectroscopy

This paper describes a method to combine near-infrared spectroscopy and a three layer back-propagation artificial neural network in order to identify official and unofficial rhubarbs. Thirty-three samples were taken as the training set, and 62 samples as the test set. The effects of input node number, learning rate and momentum on the final error and recognition accuracy for the training set, and on prediction accuracy for the test set were determined. A neural network with eight input nodes, a 0.5 learning rate, and a momentum of 0.3 can achieve a recognition accuracy of 100% for the training set and a prediction accuracy of 96.8% for the test set. The method described offers a quick and efficient means of identifying rhubarbs.     

基于深度残差神经网络迁移学习的牙形刺图像识别

为了探索基于深度神经网络模型的牙形刺图像智能识别效果,研究选取奥陶纪8种牙形刺作为研究对象,通过体视显微镜采集牙形刺图像1188幅,收集整理公开发表文献的牙形刺图像778幅,将图像数据集划分为训练集和测试集。通过对训练集图像进行旋转、翻转、滤波增强处理,解决了训练样本不足的问题。基于ResNet-18、ResNet-34、ResNet-50、ResNet-101、ResNet-152五种残差神经网络模型,采用迁移学习方法,对网络模型进行训练以获取模型参数,五种模型测试Top-1准确率分别为85.37%、85.85%、83.90%、81.95%、80.00%, Top-2准确率分别为94.63%、94.63%、94.15%、93.17%、93.66%,模型对牙形刺图像具有较好的识别效果。通过对比研究发现,ResNet-34识别准确率最高,说明对于特征简单的牙形刺属种,增加网络深度并不一定能提升准确率,而确定合适深度的模型则不仅可以提高识别准确率,还可以节约计算资源。通过ResNet-34模型的迁移学习训练和重新训练效果对比可以看出,迁移学习不仅可以获得较高的准确率,而且可以较快获取模型参...     

A chaotic neural network mimicking an olfactory system and its application on image recognition

1 Introduction A biological neural system is complicated and ef-ficient. People have tried for years to simulate it to per-form complex signal processing functions. For example,the artificial neural network is a kind of model derivedfrom a biological neural system. Most artificial neuralnetworks simulate some important features such as thethreshold behaviour and plasticity of synapses. However,they are primary simulations and still much simpler incomparison with specific biological neural…     

基于蛋白质二级结构内容的域结构类预测

运用加入竞争层的BP网络,研究了基于蛋白质二级结构内容的域结构类预测问题.在BP网络中嵌入一竞争,层显著提高了网络预测性能.仅使用了一个小的训练集和简单的网络结构,获得了很高的预测精度自支持精度97.62%,jack-knife测试精度97.62%,及平均外推精度90.74%.在建立更完备的域结构类特征向量和更有代表性的训练集的基础上,所述方法将为蛋白质域结构分类领域提供新的分类基准.     

Izhikevich神经元网络的同步与联想记忆

联想记忆是人脑的一项重要功能。以Izhikevich神经元模型为节点,构建神经网络,神经元之间采用全连结的方式;以神经元群体的时空编码（spatio-temporal coding）理论研究所构建神经网络的联想记忆功能。在加入高斯白噪声的情况下,调节网络中神经元之间的连接强度的大小,当连接强度和噪声强度达到一个阈值时网络中部分神经元同步放电,实现了存储模式的联想记忆与恢复。仿真结果表明,神经元之间的连接强度在联想记忆的过程中发挥了重要的作用,噪声可以促使神经元间的同步放电,有助于神经网络实现存储模式的联想记忆与恢复。     

Visual threshold is set by linear and nonlinear mechanisms in the retina that mitigate noise: how neural circuits in the retina improve the signal-to-noise ratio of the single-photon response

In sensory biology, a major outstanding question is how sensory receptor cells minimize noise while maximizing signal to set the detection threshold. This optimization could be problematic because the origin of both the signals and the limiting noise in most sensory systems is believed to lie in stimulus transduction. Signal processing in receptor cells can improve the signal-to-noise ratio. However, neural circuits can further optimize the detection threshold by pooling signals from sensory receptor cells and processing them using a combination of linear and nonlinear filtering mechanisms. In the visual system, noise limiting light detection has been assumed to arise from stimulus transduction in rod photoreceptors. In this context, the evolutionary optimization of the signal-to-noise ratio in the retina has proven critical in allowing visual sensitivity to approach the limits set by the quantal nature of light. Here, we discuss how noise in the mammalian retina is mitigated to allow for highly sensitive night vision.     

Improved prediction of signal peptides: SignalP 3.0

We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server