非线性系统辨识方法研究

讨论了利用小波神经网络对非线性系统辨识的新方法。在辨识过程中,为了提高小波神经网络对非线性系统的辨识性能,使用一种改进粒子群优化算法对BP小波神经网络参数进行训练,求得最优值,达到对非线性系统辨识目的。在数值仿真中,与采用标准粒子群优化算法相比,结果显示了提出的方法在收敛性和稳定性等方面均得到了明显的改善。     

基于小波神经网络的定子电阻参数辨识的研究

提出了基于小波神经网络的定子电阻在线检测方案 ,小波神经网络采用递推正交最小二乘法训练之后 ,可用于对定子电阻的辨识 .仿真实验结果表明该辨识器能够精确辨识定子电阻值 ,从而有效地改善了直接转矩控制系统的低速性能 .     

前向神经网络信赖域学习算法的研究

本文介绍了引入信赖域优化理论解决神经网络中学习问题的新算法,提出了计算有效信赖域步方法,以保证信赖域算法的正确性,采用变系数方法避免了信赖域半径自适应调整过程中不稳定和低效的问题。实验表明,信赖域学习算法优于变尺度算法。     

基于LSFD算法的小波模糊神经网络的研究与应用

利用小波变换的多分辨率特性构造小波模糊神经网络模型,并应用在非线性系统的辨识上.在参数学习上,给出了模糊微分与李亚普诺夫稳定相结合的新算法—LSFD算法,并与梯度下降法进行了对比.通过仿真,结果表明小波模糊神经网络模型与模糊神经网络、模糊小波神经网络、小波神经网络和神经网络等模型相比,其性能指标最小,收敛速度更快,更加准确.     

基于PSO算法的WNN大包线增益调参控制律设计

提出了一种基于粒子群优化算法的小波神经网络大包线调参控制律设计方法.该方法用小波函数代替了Sigmoid函数作为激活函数.由于结合了小波变换良好的高频域时间精度、低频域频率精度的性质和神经网络的自学习功能,因而具有较强逼近非线性函数的能力.为了克服局部极小值问题并进一步提高对非线性函数逼近能力,利用粒子群优化算法对小波神经网络进行参数训练,并利用该网络实现了大包线增益调参.飞行仿真结果表明,所设计的小波神经网络增益调参控制器具有优良的控制性能,不仅能够保证平衡状态下的控制效果,而且在未训练的平衡状态下依然具有良好的控制性能,并且在存在20%的建模误差时,最大超调量仅为6 m,仅是使用常规增益调参方法的18%.     

神经网络检测的数字语音水印算法

研究数字水印提高抗击性能,由于音频水印容量有限,同时盲检测方案中抗攻击能力普遍较弱,针对上述问题提出了一种在小波域中利用神经网络检测的数字语音水印算法.在数字语音载体的重要小波系数中隐藏了一幅不可感知的二值数字图像,通过二值数字图像中附加的模板对神经网络进行训练,经过训练后的神经网络几乎能够完全恢复嵌入到数字语音中的水印数据.仿真结果表明,小波域水印容量得到提高,算法对抗高斯噪声攻击、压缩、重采样等攻击,具有较强的鲁棒性,可以为实际应用提供依据.     

神经网络泛化性能的最优脑外科过程算法

在神经网络性能测试新方法的研究中,关于最优脑外科过程拥有较高的权值修剪准确率和节点压缩率的问题,但其训练和泛化优化的异步影响了算法的实际应用.把剪枝条件以约束项的形式纳入神经网络的训练日标函数中,借鉴信赖域的思路和正则化方法,设计了含约束项的最优脑外科过程.经验证,过程在理论上是收敛的.通过雷文博格-马括特(Leven-berg-Marquardt)方法实现了该过程,典型函数仿真实验验证了过程不仅提高了神经网络的泛化性能,实现了网络训练与最优脑外科剪枝的并行,也说明了信赖域的方法与雷文博格-马括特方法在理论上的一致性.     

基于混合粒子群算法的小波神经网络故障诊断

针对复杂的飞控系统传感器故障类型,建立了故障诊断模型,提取了各种故障数据.构建3层小波神经网络,并提出一种改进粒子群算法--混合粒子群算法对小波神经网络进行训练,该算法使用离散粒子群算法优化小波神经网络连接结构,同时使用基本粒子群优化算法优化小波神经网络权值.将这种改进的小波神经网络算法应用于飞控系统传感器故障诊断中....     

基于SA-PSO的小波神经网络汽轮机故障诊断

针对传统小波神经网络的问题,提出了一种基于模拟退火粒子群算法优化小波神经网络并用于汽轮机故障诊断.先使用模拟退火粒子群算法对小波神经网络的参数进行初步优化,再用小波神经网络进行二次优化训练.实验结果表明,所提出的SA-PSO-WNN算法与WNN、PSO-WNN算法相比,网络的训练速度更快,全局搜索能力更强,网络的泛化能力更好,具有很好的实用价值.     

基于神经网络的小波域数字水印嵌入算法研究

提出一种结合神经网络将二值水印嵌入离散小波变换后的宿主图像中的新方法.为使算法具有更好的不可感知性和鲁棒性,进一步提高它的实用性,结合神经网络理论,创新地提出在小波域实现对数字水印嵌入.该方法是对宿主图像做离散小波分解,取分解后的近似分量作为嵌入位置.在其中随机的选取一些像素点及其邻域,利用神经网络对其进行建模及训练,通过修改其像素值嵌入水印信息.在嵌入之前对二值水印进行了Arnold变换来加密.实验结果表明,算法具有很强的抗几何攻击和承受其他图像处理操作的能力,不可感知性好,鲁棒性明显优于一般小波域嵌入算法,对数字水印的实现具有很强的参考价值.     

Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.     

A sliding window solution for the on-line implementation of the Levenberg–Marquardt algorithm

The Levenberg–Marquardt algorithm is considered as the most effective one for training artificial neural networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true iterative version to use in on-line training. The algorithm is frequently used for off-line training in batch versions although some attempts have been made to implement iterative versions. To overcome the difficulties in implementing the iterative version, a batch-sliding window with Early Stopping, which uses a hybrid Direct/Specialized evaluation procedure, is proposed and tested with a real system.     

Distributed cooperative learning algorithms using wavelet neural network

This paper investigates the distributed cooperative learning (DCL) problems over networks, where each node only has access to its own data generated by the unknown pattern (map or function) uniformly, and all nodes cooperatively learn the pattern by exchanging local information with their neighboring nodes. These problems cannot be solved by using traditional centralized algorithms. To solve these problems, two novel DCL algorithms using wavelet neural networks are proposed, including continuous-time DCL (CT-DCL) algorithm and discrete-time DCL (DT-DCL) algorithm. Combining the characteristics of neural networks with the properties of the wavelet approximation, the wavelet series are used to approximate the unknown pattern. The DCL algorithms are used to train the optimal weight coefficient matrix of wavelet series. Moreover, the convergence of the proposed algorithms is guaranteed by using the Lyapunov method. Compared with existing distributed optimization strategies such as distributed average consensus (DAC) and alternating direction method of multipliers (ADMM), our DT-DCL algorithm requires less information communications and training time than ADMM strategy. In addition, it achieves higher accuracy than DAC strategy when the network consists of large amounts of nodes. Moreover, the proposed CT-DCL algorithm using a proper step size is more accurate than the DT-DCL algorithm if the training time is not considered. Several illustrative examples are presented to show the efficiencies and advantages of the proposed algorithms.

     

Wavelet basis function neural networks for sequential learning.

In this letter, we develop the wavelet basis function neural networks (WBFNNs). It is analogous to radial basis function neural networks (RBFNNs) and to wavelet neural networks (WNNs). In WBFNNs, both the scaling function and the wavelet function of a multiresolution approximation (MRA) are adopted as the basis for approximating functions. A sequential learning algorithm for WBFNNs is presented and compared to the sequential learning algorithm of RBFNNs. Experimental results show that WBFNNs have better generalization property and require shorter training time than RBFNNs.     

Wavelet networks

A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions. The basic idea is to replace the neurons by ;wavelons', i.e., computing units obtained by cascading an affine transform and a multidimensional wavelet. Then these affine transforms and the synaptic weights must be identified from possibly noise corrupted input/output data. An algorithm of backpropagation type is proposed for wavelet network training, and experimental results are reported.     

一种基于神经网络的盲水印方法

该文提出一种在图像小波变换的低频系数中嵌入水印的方法,水印的提取是基于神经网络方法,而且不依靠原始输入图像。实验结果表明,该方法所实现的水印图像视觉效果好,且对常见的图像处理是稳健的。     

Artificial wavelet neural network and its application in neuro-fuzzy models

In the proposed work, two types of artificial neural networks are proposed by using well-known advantages and valuable features of wavelets and sigmoidal activation functions. Two neurons are derived by adding and multiplying the outputs of the wavelet and the sigmoidal activation functions. These neurons in a feed-forward single hidden layer network result summation wavelet neural network (SWNN) and multiplication wavelet neural network (MWNN). An algorithm is introduced for structure determination of the proposed networks. Approximation properties of SWNN and MWNN have been evaluated with different wavelet functions. The above networks in the consequent part of the neuro-fuzzy model result summation wavelet neuro-fuzzy (SWNF) and multiplication wavelet neuro-fuzzy (MWNF) models. Different types of wavelet function are tested with the proposed networks and fuzzy models on four different dynamical examples. Convergence of the learning process is also guaranteed by adaptive learning rate and performing stability analysis using Lyapunov function.     

运用神经网络对音频数据索引的最优基的选择

在详细探讨了反向传播训练算法之后，提出了用神经网络选择音频数据索引最优基的方法．该方法用小波变换抽取音频信号的关键系数，根据四层小波包二分树确定输出神经元的数量与含义，用Levenberg—Marquardt修正反向传播算法构造与训练了一个32—8—8人工神经网络．试验表明，可以用该神经网络代替复杂的代价函数方法来选择音频数据索引的最优基．