小波神经网络在农药荧光光谱识别中的应用

对于结构非常相似的农药,它们的荧光光谱也非常相似并且在很宽波长范围内相互重叠.传统的荧光光谱分析法很难对其进行分类识别.一种基于小波分析而构造的新型神经网络--小波神经网络是利用它并适当选取网络结构和小波基,实现了对卡死克、盖虫散和吡虫啉三种农药荧光光谱的分类识别.实验表明,小波神经网络对光谱间的细微结构差别具有良好的识别能力.通过比较发现,在分类识别方面小波神经网络比BP网络具有更高的分辨率及较少的训练次数.     

基于神经网络的小波分析及其在 突发噪声识别中的应用􀀁

神经网络具有良好的自适应性,自组织性及很强的学习功能,小波则提供了一种去除噪声的方法,但单元基于此算法的软件往往缺少对于突发性噪声的自适应能力,本文将此二者有全,以神经网络原理和现代数学小波分析为依据,提出了基于神经网络思想的小波分析;以改进原有算法,识别工程技术测量中遇到的突发噪声。     

小波分析和神经网络在水下目标识别中的研究

研究水下目标识别问题.由于环境因素的影响,采集到的水下目标回波信号中含有大量噪声且信号频率范围大,传统方法不能有效提取信号特征导致水下目标识别率低.为了提高水下目标识别的准确率,提出一种基于小波分析和BP神经网络组合的水下目标识别方法(W-BPNN).采用小波对水下目标回波信号进行去噪处理,滤除噪声信号.通过小波包对信号的特征进行提取,提取出最能反映目标本质性质的特征向量,对提取的特征向量作为BP神经网络的输入进行识别.为了验证W-BPNN算法有效性,在Matlab平台上对3类水下目标进行了仿真.结果表明,相对于传统识别算法,W-BPNN获得了更高的识别准确率,证明是有效的水下目标识别方法.     

小波神经网络在黄金价格预测中的应用

通过对影响黄金价格变动的主要因素的研究,提出一种基于小波神经网络的黄金价格预测模型。给出了具体的网络学习算法,并结合算法对黄金价格进行预测。为验证模型有效性,进行了对比测试。分析结果表明,小波神经网络模型比传统的BP神经网络模型具有收敛速度快、预测精度高的特点。     

小波神经网络在粮食产量预测中的应用

文章提出一种基于小波神经网络的粮食产量预测模型。给出具体的网络学习算法,并结合算法对我国粮食产量进行预测。为验证模型有效性,进行了对比测试。分析结果表明,小波神经网络模型比传统的BP神经网络模型具有收敛速度快,预测精度高的特点。     

小波神经网络在手写数字识别中研究与应用

针对手写数字识别的特点,讨论了数字识别预处理的方法,包括二值化、倾斜矫正、细化和归一化。利用小波函数代替传统神经网络中的激活函数,构建了用于数字识别,小波神经网络系统。仿真结果显示,新系统大大提高了网络训练速度,数字识别的正确率也明显提高。     

基于小波分析和概率神经网络的心音诊断研究

心音对大多数心血管疾病具有极高的临床诊断价值,对心音信号进行分析有助于临床上对心脏疾病的诊断。为了利用计算机智能分析心音信号,提出利用多尺度小波分解消除信号中的噪声,从各频带提取特征值,用概率神经网络（PNN）来进行心音信号的自动分析诊断。用Matlab仿真的方法测试了5种不同类型心音信号的分类情况,结果表明该方法可行。     

小波神经网络在过程控制中的应用

在工业过程控制中,常常存在一些重要的变量难以测量,为了解决这个问题,出现了软仪表.软仪表的实质是建立被测量参数与影响该参数的其它操作参数之间的数学模型,通过计算得出此类难于测量的变量的数值.小波神经网络就是软测量的一种方法.在传统的小波神经网络的基础上进行了改进,利用小波对工业现场过来的数据进行了降噪,并使用主元分析法去除了数据的相关性.然后对处理过的数据建立小波神经网络模型,最后通过计算机仿真证实了该方法的良好的收敛速度快,不容易陷入极度最小等辨识效果.     

小波包熵和BP神经网络在意识任务识别中的应用

探索了小波包熵和BP神经网络在识别左右手想象运动中的作用.采用脑-机接口2003竞赛中Graz科技大学提供的脑电数据,计算C3、C4电极8～16Hz频带脑电信号的小波包熵,将其作为反应想象左右手运动的特征量,用BP神经网络对大脑想象左右手运动任务进行分类,最大分类正确率可达88.57%,与使用线性判别式算法分类结果相比,效果更好.脑电信号小波包熵随时间的变化与事件相关去同步和事件相关同步现象相一致,可在线识别左右手想象运动,为大脑运动意识任务的特征提取及肢残患者的临床康复提供了新思路.     

小波分析神经网络技术在故障诊断中的应用

本文对小波分析、神经网络及小波分析与神经网络相结合的小波网络在故障诊断中的应用进行了介绍,并指出了这一领域有待进一步研究的问题和发展趋势。     

Identification of nonlinear dynamical systems using multilayered neural networks

A novel multilayer discrete-time neural net paradigm is presented for the identification of multi-input multi-output (MIMO) nonlinear dynamical systems. The major novelty of this approach is a rigorous proof of identification error convergence that reveals a requirement for a new identifier structure and nonstandard weight tuning algorithms. The NN identifier includes modified delta rule weight tuning and exhibits a learning-while-functioning feature instead of learning-then-functioning, so that the identification is on-line with no explicit off-line learning phase needed. The structure of the neural net (NN) identifier is derived using a passivity aproach. Linearity in the parameters is not required and certainty equivalence is not used. The notion of persistency of excitation (PE) and passivity properties of the multilayer NN are defined and used in the convergence analysis of both the identification error and the weight estimates.     

Identification of process disturbance using SPC/EPC and neural networks

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.     

Identification of grape diseases using image analysis and BP neural networks

Prevention and treatment of diseases are critical to improve grape yield and quality. Automatic identification of grape diseases is important to prevent insect pests timely and effectively. This study proposed an automatic detection method for grape leaf diseases based on image analysis and back–propagation neural network (BPNN). The Wiener filtering method based on wavelet transform was applied to denoise the disease images. The grape leaf disease regions were segmented by Otsu method, and morphological algorithms were used to improve the lesion shape. Prewitt operator was utilized to extract the complete edge of lesion region. Five effective characteristic parameters, namely, perimeter, area, circularity, rectangularity, and shape complexity, were extracted. The proposed recognition model for grape leaf diseases based on BPNN could efficiently inspect and recognize five grape leaf diseases: leaf spot, Sphaceloma ampelinum de Bary, anthracnose, round spot, and downy mildew. Results indicated that the proposed detection system for grape leaf diseases could be used to inspect grape diseases with high classification accuracy.

     

Identification of linear and nonlinear dynamic systems using recurrent neural networks

This paper describes the use of Elman-type recurrent neural networks to identify dynamic systems. Networks as originally designed by Elman (Cognitive Sci., 1990, 14, 179–211) and also those in which self-connections are made to the context units were employed to identify a variety of linear and nonlinear systems. It was found that the latter networks were more versatile than the basic Elman nets in being able to model the dynamic behaviour of high order linear and nonlinear systems.     

Identification of nonlinear dynamic systems using functional linkartificial neural networks

We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the input pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.     

Identification of probe request attacks in WLANs using neural networks

Any sniffer can see the information sent through unprotected ‘probe request messages’ and ‘probe response messages’ in wireless local area networks (WLAN). A station (STA) can send probe requests to trigger probe responses by simply spoofing a genuine media access control (MAC) address to deceive access point (AP) controlled access list. Adversaries exploit these weaknesses to flood APs with probe requests, which can generate a denial of service (DoS) to genuine STAs. The research examines traffic of a WLAN using supervised feed-forward neural network classifier to identify genuine frames from rogue frames. The novel feature of this approach is to capture the genuine user and attacker training data separately and label them prior to training without network administrator’s intervention. The model’s performance is validated using self-consistency and fivefold cross-validation tests. The simulation is comprehensive and takes into account the real-world environment. The results show that this approach detects probe request attacks extremely well. This solution also detects an attack during an early stage of the communication, so that it can prevent any other attacks when an adversary contemplates to start breaking into the network.     

Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD

In this paper, a novel method was put forward for automatic identification of the normal and abnormal heart sounds. After the original heart sound signal was pre-processed, it was analyzed by the optimum multi-scale wavelet packet decomposition (OMS-WPD), and then the wavelet-time entropy was applied to extract features from the decomposition components. The extracted features were then applied to a support vector machine (SVM) for identification of the normal and five types of abnormal heart sounds. To show the robustness of the proposed method, its performance was compared with four other popular heart sound processing methods. Extensive experimental results showed that the feature extraction method proposed in this paper has convincing identification results, which could be used as a basis for further analysis of heart sound.     

Identification of a two-link flexible manipulator using adaptivetime delay neural networks

This paper deals with identification of a two-link flexible manipulator belonging to a class of multi-input, multi-output (MIMO) nonlinear systems, by using adaptive time delay neural networks (ATDNNs). Two neuro-dynamic identifiers are proposed. The capabilities of the proposed structures for representing the nonlinear input-output map of the flexible manipulator are shown analytically. Selection criteria for specifying the fixed structural parameters as well as the adaptation laws for updating the adjustable parameters of the networks are provided. During identification, the two-link flexible manipulator is under nonlinear control and the input-output data sets are generated for different desired trajectories. Simulation results reveal that the proposed neuro-dynamic structures are capable of successfully identifying a highly nonlinear system without any a priori information about the nonlinearities of the system and without any off-line training.     

On the "Identification and control of dynamical systems using neural networks"

Referring to the above said paper by Narendra-Parthasarathy (ibid., vol.1, p4-27 (1990)), it is noted that the given Example 2 (p.15) has a third equilibrium state corresponding to the point (0.5, 0.5).