基于BP网络的血液气味识别模型的建立

以血液气味样品的气相色谱质谱分析结果为基础,建立了一个基于BP人工神经网络的血液气味识别模型,并利用Matlab计算平台对此模型进行了优化、训练和测试.此模型的网络结构为9×13×1,隐含层传递函数为tansig,输出层传递函数为logsig,训练函数为trainrp.优化后的模型对血液样品的正确识别率为100％.     

二次回归分析人工神经网络

在线性回归分析人工神经网络的基础上提出二次回归分析人工神经网络,用于解决二值非线性分类问题。     

人工神经网络概述

近年来,随着人工神经网络在信息、自动化、医学、经济等领域的广泛应用和杰出表现,使得人工神经网络又开始得到广泛关注和重新重视。基于需求,本文对人工神经网络做了简单概述。     

An application of an artificial neural network to reliability screen classification from noise measurement

In this paper, we suggest that the reliability screen classification of BJTs from noise measurement belongs in statistical pattern recognition, then the multilayer artificial neural network is used as reliability screen classifier. The structure of a multilayer neural network (MLNN) with a back-propagation algorithm for training weights of the MLNN is discussed. This method can obtain optimal decision regions and the minimum summed squared error. Finally, an application of a neural network to the reliability screen classification of 100 BJTs is given, the results show that the MLNN is a feasible reliability screen classifier.     

GPS GDOP classification via improved neural network trainings and principal component analysis

Geometric dilution of precision (GDOP) is an engineering expression that denotes how well the constellation of global positioning system (GPS) satellites is organised geometrically. In the analysis of received signals, it is often essential to invert and transform the data matrices. This requires tremendous computational burden on the navigator’s processor. Since classification of GPS GDOP is a non-linear problem, neural networks (NNs) can be used as an acceptable solution. Since the back propagation (BP) does not have sufficient speed to train a feed-forward NN, in this paper several improved NN trainings, including Levenberg–Marquardt (LM), modified LM, and resilient BP (RBP), scaled conjugate gradient, one-step secant (OSS) and quasi-Newton methods are proposed to classify the GPS GDOP. In this study, in order to have uncorrelated and informative features of the GPS GDOP, principal component analysis (PCA) is used as a pre-processing step. The simulation results show that using the RBP and PCA leads to greater accuracy and lower calculation time comparing with other existing and proposed methods and it can improve the classification accuracy of GPS satellites to about 99.65%. Moreover, the modified LM is the fastest algorithm that requires only 10 iterations for training the NN and it can be used in online applications.     

进化计算与人工神经网络的结合

进化计算和人工神经网络作为两个工具在众多的研究领域得到了广泛应用。进化计算和人工神经网络本身也得到了很大的发展。类似于生物神经网络,人工神经网络（ Ａ Ｎ Ｎ） 由一些简单的处理单元组成,这些单元通过权值的连接而相互作用。 Ａ Ｎ Ｎ 因其鲁棒性、并行性及学习能力受到特别的重视。进化计算体现了生物进化中的四个要素,即：繁殖、变异、竞争和自然选择。目前泛指各种基于生物进化原理的仿真计算方法的总称。文中首先介绍了进化计算的有关概念,包括遗传算法、进化策略等,其次就其与人工神经网络技术相结合的方法作了进一步分析探讨。主要集中于进化的网络结构设计、进化的网络训练及其它结合方法等方面的有关问题。     

Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network

Analog Integrated Circuits and Signal Processing - Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging...     

How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network

End-to-end traffic, which describes the inherent characteristics and end-to-end behaviors of communication networks, is the crucial input parameter of network management and network traffic engineering. This paper proposes a new reconstruction algorithm to develop the research on reconstruction of end-to-end traffic in large-scale communication networks. We firstly conduct the time-frequency analysis on end-to-end traffic, and then localize its features to gain its time-frequency properties before decomposing it into the low-frequency and high-frequency components. We find that if decomposing appropriately, the low-frequency component of end-to-end traffic can accurately reflect its change trend, while its high-frequency component can well show the burst and fluctuation nature. This motivates us to find a reasonable time-frequency decomposition strategy to extract the low-frequency and high-frequency components of end-to-end traffic. Moreover, this further inspires us to use the regressive model to model the low-frequency part, exploit artificial neural network to characterize the high-frequency component, and then combine these two parts according to the regressive model and artificial neural network to precisely reconstruct end-to-end traffic. Simulation results show that in contrast to previous methods our algorithm is much more effective and promising.     

基于神经网络的图像识别

神经网络图像识别技术是随着当代计算机技术、图像处理、人工智能、模式识别理论等发展起来的一种新型图像识别技术。在进行图像识别之前需要利用数字图像处理技术进行图像预处理以及特征提取。本文选取字符图像0~9作为识别目标,对图像预处理过程进行了叙述,并在此基础上选取字符图像矩阵每行的与每列的黑色像素点之和以及图像欧拉数这两个特征作为BP神经网络的输入样本。经实验仿真表明图像的平均识别率为89%,这表明图像预处理的结果和提取的特征是合适的、有效的,设计的BP网络也较好的完成了模式分类识别工作。     

人工神经网络在水文领域中的应用

本文以某干旱地区的河流为典型研究对象,通过构建人工神经网络的方法,对该河流的水文情况进行预测.研究结果显示,本文中所采用的BP神经网络及其算法具有较好的精度和稳定性,可用于该地区今后水文情况的预测,同时也可为其他地区的相关工作提供参考借鉴.     

基于卷积神经网络和胶囊神经网络的宫颈病变图像分类方法研究

为了更好地治疗宫颈癌,准确确定患者的宫颈类型是至关重要的。因此,用于检测和划分宫颈类型的自动化方法在该领域中具有重要的医学应用。虽然深度卷积神经网络和传统的机器学习方法在宫颈病变图像分类方面已经取得了良好的效果,但它们无法充分利用图像和图像标签的某些关键特征之间的长期依赖关系。为了解决这个问题,文章引入了胶囊网络(CapsNet),将CNN和CapsNet结合起来,以提出CNN-CapsNet框架,该框架可以加深对图像内容的理解,学习图像的结构化特征,并开展医学图像分析中大数据的端到端训练。特别是,文章应用迁移学习方法将在ImageNet数据集上预先训练的权重参数传输到CNN部分,并采用自定义损失函数,以便网络能够更快地训练和收敛,并具有更准确的权重参数。实验结果表明,与ResNet和InceptionV3等其他CNN模型相比,文章提出的网络模型在宫颈病变图像分类方面更加准确、有效。     

基于灰色关联分析的SOM神经网络在葡萄分类中的应用

随着葡萄酒需求的大量增加,葡萄酒的质量受到了越来越多的关注.一般地,葡萄酒质量采用感官品尝的结果来评定,但是却经常受到多种因素的影响,同时葡萄酒的质量又没有统一的标准,因此葡萄酒的质量评价体系的建立亟待解决,而酿酒葡萄的质量直接决定了葡萄酒的品级.为了得到较好的葡萄酒先要对葡萄进行筛选.基于葡萄的理化指标较多,用灰色关联分析对数据进行初步处理,提取出影响葡萄质量的数个主要理化指标,再运用数据挖掘中的SOM神经网络技术对葡萄进行聚类分析.仿真结果表明:SOM神经网络能够直观准确地将原27类葡萄样品分为7类,且每一类中的葡萄样品均有一定的相似性.     

人工神经网络的BP算法及其应用

在一般人工神经网络的BP算法的基础上,研究了BP算法所遵循的数学基础,指出其存在的缺陷和不足,并给出改进方法。探讨了MATLAB环境下实现人工神经网络BP算法的编程方法及方法改进比较的例子,得到了良好的结果。     

The use of wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices

This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes' entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively.     

Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach

Pneumatic Artificial Muscle (PAM) actuator has been widely used in medical and rehabilitation robots, owing to its high power-to-weight ratio and inherent safety characteristics. However, the PAM exhibits highly non-linear and time variant behavior, due to compressibility of air, use of elastic-viscous material as core tube and pantographic motion of the PAM outer sheath. It is difficult to obtain a precise model using analytical modeling methods. This paper proposes a new Artificial Neural Network (ANN) based modeling approach for modeling PAM actuator. To obtain higher precision ANN model, three different approaches, namely, Back Propagation (BP) algorithm, Genetic Algorithm (GA) approach and hybrid approach combing BP algorithm with Modified Genetic Algorithm (MGA) are developed to optimize ANN parameters. Results show that the ANN model using the GA approach outperforms the BP algorithm, and the hybrid approach shows the best performance among the three approaches.     

人工神经网络在医学统计预测中的应用

当用于预测的指标和被预测指标间是一种复杂的多元非线性关系时,可运用人工神经网络的方法进行预测,本文探讨了人工神经网络理论在医学统计预测领域中的应用方法。     

Active MEMS-based flow control using artificial neural network

These last years several research works have studied the application of Micro-Electro-Mechanical Systems (MEMS) for aerodynamic active flow control. Controlling such MEMS-based systems remains a challenge. Among the several existing control approaches for time varying systems, many of them use a process model representing the dynamic behavior of the process to be controlled. The purpose of this paper is to study the suitability of an artificial neural network first to predict the flow evolution induced by MEMS, and next to optimize the flow w.r.t. a numerical criterion. To achieve this objective, we focus on a dynamic flow over a backward facing step where MEMS actuators velocities are adjusted to maximize the pressure over the step surface. The first effort has been to establish a baseline database provided by computational fluid dynamics simulations for training the neural network. Then we investigate the possibility to control the flow through MEMS configuration changes. Results are promising, despite slightly high computational times for real time application.     

An artificial neural network for SPECT image reconstruction

An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then backprojected to form an image as the network output. The learning algorithm adjusts the weighting coefficients using a backpropagation algorithm which minimizes the mean squared error between the ideal training image and the reconstructed training image. The response of the trained network to an impulse projection resembles the ramp filter typically used with backprojection, and reconstructed images are similar to filtered backprojection images.     

基于BP神经网络的音乐分类模型

针对单一特征难以建立理想音乐分类模型的不足,为了帮助用户找到自己喜欢的音乐,提出BP神经网络的音乐分类模型。首先提取音乐的多种类型特征,便于对音乐信息进行准确描述,然后将这些特征组合在一起作为音乐分类模型的输入向量,通过BP神经网络的智能学习建立音乐分类模型,最后在Matlab 2016平台下进行多个音乐分类实验。结果表明,该模型克服了单一特征提供信息简单的局限性,提高了音乐的分类正确率,而且音乐分类的实时性较好,可以用于网络上的音乐检索研究。