基于最小二乘支持向量机的SOFC电特性建模

针对现有的固体氧化物燃料电池(SOFC)模型过于复杂的弊端,提出了一种基于最小二乘支持向量机(LS-SVM)的建模方法,用具有径向基函数(RBF)核函数的LS-SVM建立了SOFC电堆的非线性模型.应用仿真对建模的有效性和精度进行了检验,并与径向基函数神经网络(RBFNN)模型的辨识效果进行了比较.仿真结果证明,与RBFNN模型相比,LS-SVM模型具有较高的预测精度,这表明用LS-SVM对SOFC电堆进行建模是可行的.该LS-SVM模型的建立,对SOFC系统控制策略的研究具有一定的实用价值.     

机器人系统中操作环境动力学模型研究

研究了机器人操作环境的动力学模型,提出了一种基于径向基函数(RBF)神经网络的机器人系统中环境非线性动力学模型新的建立方法,阐述了其建模机理和算法.结果表明,采用RBF神经网络对机器人系统中的操作环境建模比用BP神经网络有更高的精度,其网络训练速度也大大快于BP神经网络.     

BP算法及其应用实例

BP网络(也称激活函数采用Sigmoidal函数的多层感知器)是一种按误差逆传播算法训练的多层前馈网络,是目前应用最广泛的神经网络模型之一。BP算法是一类有导学习算法,用于BP网权值和阈值学习,它的规则的推导是基于最小均方误差准则,它可实现函数逼近,同时利用Matlab进行仿真。     

复合基神经网络在水声目标分类识别中的应用

本文根据不同神经网络的分类特点，提出将径向基函数网络和多层感知器网络复合构成复合基网络，用于水声信号的分类识别，试验表明，该网络的分类能力及对未来训练目标的适应性优于ＢＰ网和ＲＢＦ网。     

基于径向基函数神经网络的滚动轴承故障模式的识别

径向基函数（RBF）神经网络是一种3层前馈性神经网络，它具有较强的函数逼近能力和分类能力。鉴于径向基函数神经网络的优点，在对滚动轴承振动信号特征分析的基础上，提出了采用时序方法对其建立AR模型，利用AR模型参数建立径向基函数神经网络，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

红外光谱结合主成分分析对纸质快递文件袋的分类研究

目的 建立一种快速无损的检验纸质快递文件袋的分析方法。方法 利用傅里叶变换红外光谱对63个纸质快递文件袋样品进行检验，分析样品的红外光谱吸收峰的峰位，结合主成分分析对光谱数据进行了降维处理并分类。利用费歇尔判别对快递文件袋的分类结果进行分析和验证。同时建立多层感知器神经网络和径向基函数神经网络2种分类模型，进行分析和验证。结果 63个纸质快递文件袋样品可被分成四大类，利用费歇尔分类模型进行验证，准确率为100%；多层感知器神经网络分类模型准确率为95.23%，径向基函数神经网络分类模型准确率为92.06%。通过比较发现，费歇尔判别可以实现对纸质快递文件袋更加有效地分类。结论 该方法简单快速，样品用量少且无损样品，可为快递文件袋类的物证鉴定提供科学依据。     

径向基函数预测网络拓扑确定新方法

预测能力相对薄弱,已经成为制约PHM(Prognostics and Health Management)技术发展和应用的瓶颈.随着传感器和BIT(Built-in Test)设计技术的日益进步,采用序列分析的方法对复杂系统装备进行故障预测已经成为可能.在基于序列分析的预测方法研究中,径向基函数预测网络具有结构简单、学习速度快、具备非线性建模能力等诸多优点.为了改进其预测性能,在深入分析网络拓扑对模型性能及建模时间影响的基础上,综合考察了序列最佳线性自相关长度、建模精度和模型复杂度等多种因素,提出了基于偏自相关函数统计检测的输入层节点数目确定算法和基于BIC(Bayesian Information Criteria)准则的隐层节点数目确定算法,用以构建径向基函数预测网络;并对算法的有效性进行了分析.仿真结果表明,同传统建模算法相比较,由新算法构建的径向基函数预测网络具有最佳的预测性能,且建模时间不足传统算法的3%.     

基于RBF神经网络的水轮发电机组动态建模

针对水轮发电机组具有复杂动态特性和不确定性难以精确建原特点，本文提出了采用径向基函数神经网络的水轮发电机组动态建模算法，并进行了仿真实验。实验结果表明用ＲＢＦ神经网络可方便地建立复杂对象４的动态模型，而且具有较高的精确度。     

基于神经网络的图像混合滤波及融合算法研究

当图像中同时存在高斯噪声和椒盐噪声时,单一的均值滤波或中值滤波很难达到最佳滤波效果。 分析了噪声特点和各种滤波方法的优势,提出了一种基于神经网络的图像混合滤波及融合算法:首先建立概率神经网络,检测椒盐噪声和高斯噪声点,并分别利用中值滤波和均值滤波去除噪声点,然后建立径向基函数神经网络,利用训练好的径向基函数神经网络融合 2 种不同滤波的图像,输出理想的融合图像。 Matlab 仿真实验结果表明,该算法有效去除混合噪声的同时,能很好地保护图像的边缘与细节,是一种有效的方法。     

实现CDMA最佳多用户检测的神经网络方法

提出并讨论了两种实现码分多址系统中最佳多用户检测的神经网络方法。一种基于离散Ｈｏｐｆｉｅｌｄ神经网络，另一种基于采用反向传播算法的多层感知器神经网络。理论分析和计算机模拟都证实了这两种神经网络方法的可行性，优越性和实用性。前者适用“固定”用户情况；后者既可用于“固定”用户吼适用于移动用户的情况，因而在未来的ＣＤＭＡ个人通信网中有较好的应用前景。     

On reaction network implementations of neural networks

This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properties including (i) existence of unique positive fixed points that are smooth in the parameters of the model (necessary for gradient descent) and (ii) fast convergence to the fixed point regardless of initial condition (necessary for efficient implementation). We do so by first making a connection between neural networks and fixed points for systems of ODEs, and then by constructing reaction networks with the correct associated set of ODEs. We demonstrate the theory by constructing a reaction network that implements a neural network with a smoothed ReLU activation function, though we also demonstrate how to generalize the construction to allow for other activation functions (each with the desirable properties listed previously). As there are multiple types of ‘networks’ used in this paper, we also give a careful introduction to both reaction networks and neural networks, in order to disambiguate the overlapping vocabulary in the two settings and to clearly highlight the role of each network’s properties.     

基于自适应模糊神经网络的DMFC温度建模和控制

针对DMFC电堆的实时控制要求，应用自适应模糊神经网络技术对DMFC电堆的工作温度进行辨识建模和控制。在温度控制过程中，将训练好的网络模型作为DMFC电堆控制系统的参考模型，并对控制模型的参数进行在线自适应调整。仿真结果表明所设计的自适应模糊神经控制器性能优越。     

MCFC型燃料电池的神经网络建模与基于FGA的模糊控制研究

为了提高燃料电池的发电性能,熔融碳酸盐燃料电池(MCFC)堆的运行温度应该控制在一个合适的范围内。本文首先利用RBF神经网络辨识复杂非线性系统的能力,基于实验的输入输出数据,建立起MCFC电堆的神经网络温度模型;然后设计了MCFC电堆工作温度的一个基于模糊遗传算法的在线模糊控制器,用模糊遗传算法同时优化模糊控制器的参数及规则。最后用神经网络的辨识模型代替实际的电堆进行控制仿真,仿真结果证明建模是有效的,所设计的模糊控制器具有较好的性能。     

基于BP神经网络的盲均衡器设计

为构建用于某语音信号传输系统盲均衡器的BP神经网络模型,编写了基于BP神经网络的盲均衡算法伪代码,计算了算法的时间复杂度,分析了BP神经网络输入层神经元个数、隐含层神经元个数和隐含层层数对盲均衡算法性能的影响,评估了基于Sigmoid的变步长算法、基于误差补偿的变步长算法和基于误差的变步长算法对基于BP神经网络的盲均衡器性能的改进效率,据此设计了一种含双隐层结构的BP神经网络盲均衡器,并对其性能进行了数值仿真分析,明确了其适用范围,为该语音信号传输系统设计提供了技术支撑。     

A Neural Network Approach to Find The Cumulative Failure Distribution: Modeling and Experimental Evidence

The failure prediction of components plays an increasingly important role in manufacturing. In this context, new models are proposed to better face this problem, and, among them, artificial neural networks are emerging as effective. A first approach to these networks can be complex, but in this paper, we will show that even simple networks can approximate the cumulative failure distribution well. The neural network approach results are often better than those based on the most useful probability distribution in reliability, the Weibull. In this paper, the performances of multilayer feedforward basic networks with different network configurations are tested, changing different parameters (e.g., the number of nodes, the learning rate, and the momentum). We used a set of different failure data of components taken from the real world, and we analyzed the accuracy of the approximation of the different neural networks compared with the least squares method based on the Weibull distribution. The results show that the networks can satisfactorily approximate the cumulative failure distribution, very often better than the least squares method, particularly in cases with a small number of available failure times. Copyright © 2015 John Wiley & Sons, Ltd.     

改进遗传算法结合FLANN在加速度传感器动态建模中的应用

对遗传算法（CA）的交叉和变异操作进行改进，提出利用改进遗传算法（ICA）和函数连接型人工神经网络（FLANN）相结合实现加速度传感器的动态建模的新方法。该方法利用加速度传感器的动态标定数据，采用IGA和FLANN相结合搜索和优化动态模型参数。文中介绍动态建模原理以及算法，给出用IGA和FLANN相结合建立的加速度传感器动态数学模型。结果表明：上面提出的动态建模方法既保留了CA的全局搜索能力和FLANN结构简单的特点，又具有网络训练速度快、实时性好、建模精度高等优点，在动态测试领域具有重要应用价值。     

FPGA Implementation of Deep Leaning Model for Video Analytics

In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware. Field programmable Gate arrays (FPGA) is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit (GPU) in terms of energy efficient and low computational complexity. But still, an intelligent architecture is required for implementing the CNN in FPGA for processing the videos. This paper introduces a modern high-performance, energy-efficient Bat Pruned Ensembled Convolutional networks (BPEC-CNN) for processing the video in the hardware. The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures (DFS) for handing the filter layers in CNN with pipelined data-path in FPGA. In addition, the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity. The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards (number) and various algorithms centric parameters such as accuracy, sensitivity, specificity and architecture centric parameters such as the power, area and throughput are analyzed. These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25% better performance than the existing architectures.     

一种用细胞神经网络提取干涉条纹中心的新方法

提取干涉条纹的中心是干涉测量的关键环节,文中提出了一种基于细胞神经网络（CNN）提取干涉条纹中心的新方法.CNN是一种实时处理信号的大规模非线性模拟电路,同时它的局部联接特点使其适用于超大规模集成电路的实现.CNN具有并行运算的能力,可消除传统串行算法复杂性高、不能实时处理的缺点.对该方法进行了分析,给出了实例的仿真结果,证明该方法能快速准确地提取干涉条纹的中心,提高了干涉条纹的判别精度,从而增加了实验中干涉条纹处理的直观性和实时性.     

BP神经网络在复合材料研究中的应用

人工神经网络因能处理复杂的非线性问题而成为材料科学研究的一种重要方法.在介绍BP神经网络的基础上,综述了其在复合材料设计、工艺优化、性能预测、损伤检测及预测等方面的应用情况,分析了应用中存在的问题,展望了其发展趋势.     

HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the entire network will fail. This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention (HDLIDP) framework, which blends signature-based and deep learning neural networks to detect and prevent intrusions. This framework improves detection accuracy while addressing all of the aforementioned problems. To validate the framework, experiments are done on both traditional and SDN datasets; the findings demonstrate a significant improvement in classification accuracy.