一种用于函数学习的小波神经网络

在非线性系统辨识中,系统输入往往是多变量的.小波处理此类问题则比较复杂.结合 神经网络形式和小波特点建立一种新型的网络,可简单有效地解决网络多输入问题.同时给出 此网络可以逼近任意连续函数的数学证明.并通过实例验证了此方法的正确性.     

小波模糊神经网络在非线性函数逼近中的应用

将前馈神经网络与T-S模糊模型相融合构造了一种模糊神经网络,进一步利用小波变换的压缩特性与模糊神经网络相结合构造出一种小波模糊神经网络模型,并应用在非线性函数逼近上。通过仿真,结果表明小波模糊神经网络是最优的。     

基于能量密度的小波神经网络

本文提出了基于能量密度构造单隐层前向小波网络用以逼近复杂非线性函数。在时频定位分析的基础上，引入了能量密度的概念，用其作为选择小波元的标准。在本文中给出了网络构造算法及相应的学习算法，并与其它小波网及ＢＰ网进行了比较。实验结果证明了该方法是可行的，且具有小波元数目相对较少、学习收敛速度快等特点，并就其在实际应用中应注意的问题提出了我们的观点。     

非线性函数逼近小波神经网络的优化及仿真

提出一种用于非线性函数逼近的小波神经网络的训练算法。分析了网络的拓扑结构,给出了网络的参数估计方法,即混合递阶遗传算法,该算法是递阶遗传算法和多元线性回归的结合,仿真研究表明该方法逼近精度高,为非线性系统建模提供了一种新方法。     

基于小波模糊网络的非线性函数逼近方法的研究

针对非线性函数逼近问题,提出了一种新的融合策略——小波模糊网络;该网络将模糊模型引入小波网络,采用正交最小二乘法筛选小波,利用推广卡尔曼滤波算法调整网络非线性参数,避免陷入局部最优,提高学习速度,并采用最小二乘法修正权值,在不增加小波基函数的基础上提高网络的逼近精度;通过仿真,该网络的准确性和泛化能力都优于传统的小波神经网络,具有广泛的应用前景。     

一种用于函数优化的小波混沌神经网络

混沌神经网络能有效地解决函数优化问题。通过把sigmoid函数转化为墨西哥帽小波函数,而单一化退火因子函数被分段指数模拟退火函数所取代,提出了一种新型的混沌神经网络。与传统的混沌神经网络相比,该网络具有更强的全局寻优能力。仿真结果表明,小波混沌神经网络在搜索全局最优解的速度和精确度上都明显优于传统的混沌神经网络。     

小波神经网络逼近能力及Thau 定理推广

首先提出神经元数目有限的小波神经网络对一大类Ｌｉｐｓｃｈｉｔｚ函数的逼近能力定理;然后对Ｔｈａｕ定理进行推广,得到几个实用性较强的推广定理;最后通过构造一种基于推广Ｔｈａｕ定理的小波神经网络非线性观测器,展示出该逼近定理的应用前景。     

基于小波神经网络的HRTFs逼近与声频仿真

头部相关传递函数是指从声源到耳鼓的谱滤波器,它因提取了声源的方位信息,所以在声频仿真中,它是非常重要的立体听觉定位曲线。由于ＨＲＴＦｓ随声源的相对位置、频和听觉对象不同而变化,并与其自变量之间还存在着非常复杂的非线性关系,所以三维声音仿真的实现需要处理庞大的ＨＲＴＦｓ的数据。     

基于小波神经网络的系统辨识方法

神经网络由于具有良好的自学习和自适应能力，在非线性黑箱建模或系统辨识中有着广泛的应用，这些辨识模型有：多层感知器、径向基函数网和反馈网络等等。文中提出了基于小波神经网络模型的系统辨识方法。由于小波变换或分解所表面的良好的时频局部化特性，以及多尺度的功能，我们用规范正交的小波函数作为基函数网络中的基函数，得到所谓的小波神经网络。通过计算机仿真证实了该方法的良好的辨识效果。     

基于模糊RBF神经网络的函数逼近

提出了一种模糊RBF网络,将模糊逻辑的知识表达以及推理能力和RBF网络的快速学习和泛化能力结合起来,网络结构参数可按实际问题调整,仿真表明网络具有较快的学习速度和较高的函数逼近精度。     

Software development cost estimation using wavelet neural networks

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost and quality of software product under development. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper, we propose the use of wavelet neural network (WNN) to forecast the software development effort. We used two types of WNN with Morlet function and Gaussian function as transfer function and also proposed threshold acceptance training algorithm for wavelet neural network (TAWNN). The effectiveness of the WNN variants is compared with other techniques such as multilayer perceptron (MLP), radial basis function network (RBFN), multiple linear regression (MLR), dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector machine (SVM) in terms of the error measure which is mean magnitude relative error (MMRE) obtained on Canadian financial (CF) dataset and IBM data processing services (IBMDPS) dataset. Based on the experiments conducted, it is observed that the WNN-Morlet for CF dataset and WNN-Gaussian for IBMDPS outperformed all the other techniques. Also, TAWNN outperformed all other techniques except WNN.     

基于小波神经网络的云模型

云模型是一种基于语言规则的不确定性推理系统.为了提高辨识精度通常需要增加规则数目,这样在多维输入的情况下容易形成"维数灾".为了解决此问题,利用小波神经网络代替传统云模型的后件隶属云,建立了一种基于小波神经网络的云模型(WNCM).详细分析了WNCM的系统结构,同时给出了参数和结构辨识算法.仿真结果以及与其它方法的对比分析表明,WNCM具有较强的非线性函数逼近能力,在不增加推理规则的前提下,可以实现对系统的精确辨识.     

From local neural networks to granular neural networks: A study in information granulation

In this study, we are concerned with a construction of granular neural networks (GNNs)—architectures formed as a direct result reconciliation of results produced by a collection of local neural networks constructed on a basis of individual data sets. Being cognizant of the diversity of the results produced by the collection of networks, we arrive at the concept of granular neural network, producing results in the form of information granules (rather than plain numeric entities) that become reflective of the diversity of the results generated by the contributing networks. The design of a granular neural network exploits the concept of justifiable granularity. Introduced is a performance index quantifying the quality of information granules generated by the granular neural network. This study is illustrated with the aid of machine learning data sets. The experimental results provide a detailed insight into the developed granular neural networks.     

基于小波神经网络的板形板厚综合系统逆控制

针对板形板厚综合系统具有强耦合、非线性、含纯滞后环节的特点,提出一种基于小波神经网络的逆控制方案.利用两个结构相同的小波神经网络构造Smith预估器,预估器的输入参数与时延阶次无关,能较好地解决小波神经网络对维数较为敏感的问题.采用神经网络逆控制的思想设计小波神经网络控制器,引入多步预测性能指标函数对控制器权值进行在线训练.仿真研究表明,该控制方案具有较快的响应速度和良好的动态性能.     

电力系统小波神经网络负荷频率控制器的研究

将小波网络用于电力系统负荷频率辨识和控制中，建立了非线性的电力系统负荷频率控制LFC模型，用递归NARMA模型的小波网络辩识器对LFC模型进行了辩识，利用Akaike’s的最终预测误差准则FPE和信息准则AIC，进行了隐层节点数目和反馈阶次的计算，用辩识结果建立了NARMA模型的小波网络的控制器，对LFC模型进行控制，理论和仿真表明辩识和控制模型可取得较好效果。     

基于模糊熵的BP算法改进

由于BP网络简单的拓扑结构和优秀的逼近能力,它已经被广泛地应用于预测和非线性系统的建模中。但是由于算法自身的不足,在实际应用中会产生很多问题。因此,BP网络的优化已经成为了一个重要的课题。为了提高BP网络的泛化能力,将模糊熵加入到BP网络的性能函数中,提出了基于模糊熵的BP算法。在实验中,将两种算法进行了对比,结果表明改进算法可以有效地提高测试精度,避免了过度拟合。     

Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit.     

Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

In this study, differential evolution algorithm (DE) is proposed to train a wavelet neural network (WNN). The resulting network is named as differential evolution trained wavelet neural network (DEWNN). The efficacy of DEWNN is tested on bankruptcy prediction datasets viz. US banks, Turkish banks and Spanish banks. Further, its efficacy is also tested on benchmark datasets such as Iris, Wine and Wisconsin Breast Cancer. Moreover, Garson’s algorithm for feature selection in multi layer perceptron is adapted in the case of DEWNN. The performance of DEWNN is compared with that of threshold accepting trained wavelet neural network (TAWNN) [Vinay Kumar, K., Ravi, V., Mahil Carr, & Raj Kiran, N. (2008). Software cost estimation using wavelet neural networks. Journal of Systems and Software] and the original wavelet neural network (WNN) in the case of all data sets without feature selection and also in the case of four data sets where feature selection was performed. The whole experimentation is conducted using 10-fold cross validation method. Results show that soft computing hybrids viz., DEWNN and TAWNN outperformed the original WNN in terms of accuracy and sensitivity across all problems. Furthermore, DEWNN outscored TAWNN in terms of accuracy and sensitivity across all problems except Turkish banks dataset.     

Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).     

Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks

The healthy operations of mechanical systems are crucially important for ensuring human safety and economic benefits, so that there is a high demand on the automatic fault diagnosis techniques. However, the number of available faulty samples of mechanical systems is often far less than healthy samples, and thereby the traditional data-driven methods often suffer a high rate of misdiagnosis. In this paper, a new fault diagnosis method is developed on the basis of wavelet packet distortion and convolutional neural networks. First, wavelet packet distortion means that wavelet packet coefficients are distorted to augment fault samples, in order to achieve the equilibrium between healthy and faulty classes. Second, a convolutional neural network-based classification model is trained using the balanced training dataset. Third, the trained model is applied to classify the testing samples. Finally, the efficacy of this developed method in imbalanced fault diagnosis of mechanical systems is demonstrated through a number of experiments.