基于遗传小波神经网络的冷轧轧制力预报研究

依据小波函数的非线性逼近能力和神经网络的自学习特性，提出一种适合高维输入的小波神经网络建模方法，这种网络结构类似于多层感知器，不同的是隐层神经元的激励函数为小波基函数．为使小波神经网络具有更高的学习精度和更快的收敛速度，将遗传算法、小波神经网络和梯度下降算子结合起来，提出一种遗传小波神经网络．将该网络应用于冷轧轧制力的预报，仿真结果表明预报精度大为提高．     

一种小波神经网络的混沌加密算法研究

概括了小波神经网络的主要理论,将小波神经网络和混沌系统相结合,建立了一种混沌序列的生成模型,给出基于小波神经网络的混沌加密算法,最后对算法进行计算机仿真实验.结果表明小波神经网络具有更快的收敛速度和更准确的逼近能力,而基于小波神经网络的混沌加密算法具有很高的安全性.     

Pi-sigma神经网络混合学习算法及收敛性分析

将一种解决函数优化问题的混合遗传算法用于Pi-sigma神经网络的训练。这种混合算法充分利用遗传算法算法的全局搜索能力,又利用了单纯型法的局部搜索能力,因此该混合遗传算法可以使Pi-sigma神经网络更快的收敛到全局最优解,而且收敛速度比遗传算法更快。实验证明了这种算法的优越性。最后还证明了该算法可以以概率1收敛到全局最优解。     

一种基于改进遗传算法的径向基小波神经网络

为了提高神经网络进行函数拟合的精度,首先在三层径向基神经网络基础上通过增加网络层次和改变激励函数提出了一种四层径向基小波神经网络,并采用遗传算法来确定初始网络参数;其次针对遗传算法中容易早熟的缺点,在遗传算法中引入动态平衡策略,根据适应度的变化来动态改变遗传算法中交叉和变异概率,从而增加算法全局探索和局部开发的平衡能力;最后通过对函数拟合试验并与其他方法相比较表明了算法的有效性。     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

Construction of a new adaptive wavelet network and its learning algorithm

A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.     

基于遗传算法和小波神经网络的语音识别研究

小波神经网络算法(WNN)易陷入局部极小,收敛速度慢,全局搜索能力弱,而遗传算法(GA)具有高度并行、随机、自适应搜索性能和全局寻优的特点。因此,将遗传算法和小波神经网络结合起来形成一种训练神经网络的混合算法——GA-WNN算法。仿真实验结果表明,该算法有效地缩短了识别时间,提高了网络训练速度和语音的识别率。     

多层前向神经网络的快速学习算法及其应用

针对目前多层前向神经网络学习算法存在的不足,提出一种多层前向神经网络的快速学习算法,它不仅符合生物神经网络的基本特征,而且算法简单,学习收敛速度快,具有线性、非线性逼近精度高等特性.以二杆机械手逆运动学建模作为应用实例,仿真结果表明该方法是有效的,其算法与收敛速度更优于BP网络.     

求解线性约束的二次规划神经网络学习新算法

提出了一种基于0.618法求解具有线性约束的二次规划问题的神经网络学习新算法。与已有的求解线性约束的二次规划问题的神经网络学习算法相比,该算法的适用范围更广,计算精度更高。其目的是为具有线性约束的二次规划问题的求解提供一种新方法。仿真实验验证了新算法的有效性。     

小波与神经网络相结合的网络流量预测模型

针对网络流量序列的非线性和多时间尺度特性,提出了一种将小波变换与人工神经网络相结合进行网络流量预测的新模型.该模型吸取了小波变换的多分辨功能和人工神经网络的非线性逼近能力,对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,分别使用RBF神经网络和Elman神经网络进行预测,把两种预测的结果通过BP神经网络合成为最终预测结果.用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果.     

基于改进粒子群算法的模糊小波神经网络建模

随着射频功放非线性对射频前端的影响日益增大,使得功放建模变得越来越重要。提出了一种自适应模糊小波神经网络模型结构,并利用改进的粒子群优化算法,建立有记忆的功放模型。将小波函数融入到自适应模糊推理系统的模糊规则中,得到新的网络模型;在粒子群算法中引入最差位置影响因子,提高搜索效率,并进一步简化,忽略粒子的速度项,同时采用与适应度函数值相关的动态变化惯性权重,加快了收敛速度,避免出现“早熟”现象。仿真结果表明：该方法建立的功放模型误差小、精度高,能够有效地表征功放特性。     

小波神经网络在北京河湖水华预测中的应用

针对不同季节水华生长的不同特点,在对水华生长规律研究的基础上,运用小波分析对表征水华的叶绿素信号进行降噪处理,建立一种结合小波变换与神经网络相结合的水华预测模型（WANN模型）,该模型既有神经网络的自学习能力特性,又有小波的局部特性,并将其应用到北京夏季河湖水华预测中。通过小波多分辨率分析,对样本包含的信息进行充分挖掘,提取反映其变化规律的成分,有效避免了原始数据中噪声对网络的干扰,提高网络的性能,WANN模型预测结果与BP网络预测结果对比,具有较高的预测能力,从而获得相对理想的预测效果。     

小波神经网络模型的确定性预测及应用

针对神经网络模型预测结果的随机性,构建了一种紧致性小波神经网络工具箱。该方法将小波函数移植到BP网络隐层,并采用一种随机确定状态命令获得确定的预测结果。与编程实现的小波神经网络和BP网络比较,该方法适合于大批量数据训练,对数据样本的适应能力和鲁棒性强,尤其对高频随机时间序列有更好的适应能力,具有预测结果确定及实用性强等特点,可显著提高模型的训练速度、预测精度和预测效率。基于小波包变换和小波神经网络的瓦斯涌出量预测实验证明了所提方法的有效性。     

动态自适应模块化神经网络结构设计

针对全连接前馈神经网络不能有效应对时变系统的问题, 提出一种动态自适应模块化神经网络结构. 该网络采用减法聚类算法在线辨识工况数据的空间分布, 利用RBF 神经元实现对数据样本空间的划分, 并结合模糊策略将不同子样本空间的数据动态分配给不同的子网络, 最后对各子网络的输出进行集成. 该模块化网络中子网络数量和子网络规模都能根据所学时变任务动态自适应调整. 通过对不同时变系统的预测表明了该网络能够有效跟踪时变系统.     

基于Sanger算法的小波网络结构优化方法

为了解决离散小波神经网络(DWNN)节点过多、鲁棒性差的问题,基于主成份分析(PCA)的思想提出了一种规模小、抗干性强的广义小波神经网络(EWNN),并利用Sanger算法对其结构进行了优化。该算法在引出了消冗变换后,可提取出多个主成份。仿真结果表明了EWNN的非线性逼近能力及稳定性都明显优于DWNN。     

前馈神经网络的混沌BP 混合学习算法

简要分析由Logistic映射产生的混沌数以及不同混沌序列之间的概率统计特性，为混沌全局性搜索提供了依据．将一种快速BP算法与混沌优化相结合，提出了混沌BP混合算法，由于混沌Logistic映射的遍历性、随机性，使得混合算法收敛速度快，且具有全局性，采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。     

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.     

小波神经网络自学习算法用于红外图像分割

在红外动目标序列图像跟踪过程中,由于目标本身的红外特征具有较大的不可预测性,使ATR系统在目标探测阶段产生大量的虚警讯息。因此,必须设法在复杂背景抑制段将虚警探测讯息滤除掉。提出了一种新颖的基于小波神经网络构架的FLIR图像分割技术,旨在将小波变换的时-频局域特性和神经网络的自学习能力相结合,从而使FLIR图像的分割算法具有较强的逼近和容错能力。该算法在FLIR-ATR系统中得到应用,对于FLIR目标图像轮廓的提取和抑制杂散背景方面获得了良好的效果。     

A fast learning algorithm of neural network with tunable activation function

This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.     

Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part

This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT) and employing a back-propagation-based approach. The stabilizer initialization is performed using an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to use any identification process. Kundur's four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS.