基于小波神经网络的电容称重传感器非线性补偿研究

为了解决电容称重传感器的非线性问题,提出了补偿其非线性的小波神经网络方法。该方法以电容称重传感器实验数据为基础,通过小波神经网络训练来确定传感器非线性补偿网络。介绍电容称重传感器非线性补偿原理,分析网络的拓扑结构,给出网络参数训练方法。结果表明,采用小波神经网络进行电容称重传感器非线性补偿具有好的鲁棒性,网络训练速度快、精度高,并能在线补偿,在测试领域有实用价值。     

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。     

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%.     

基于小波神经网络的PID整定与应用

提出了一种基于小波神经网络整定的PID控制方法。由于小波变换具有良好的时频局部特性，神经网络具有强大的非线性映射能力，自学习、自适应等优势，采用规范正交的小波函数作为神经网络的基函数构成小波神经网络。该网络兼有小波函数的紧支性、波动性以及神经网络的非线性映射能力，自学习、自适应能力等优点，渗碳炉控制实验结果表明．用该方法整定的PID控制系统收敛速度快。逼近精度高，鲁棒性好。     

小波神经网络在黄豆含水率测量中的应用

提出一种用于多元非线性函数逼近的小波神经网络的训练算法,设计了网络拓扑结构和对其参数进行估计的混合递阶遗传算法。该方法避免了经典BP算法容易陷入局部极小和训练效率低的缺点,保持了较高的泛化精度,且收敛速度快,进而提高了测量精度。实验表明:应用小波神经网络多元非线性回归方法的含水率测量值与真实值间相对误差均方差为0.084,相对误差均值为0.117,相对误差最大值为0.159。     

基于小波神经网络的抄纸过程建模仿真研究

抄纸过程中水分定量控制是一种复杂的多变量耦合过程.它具有大滞后、非线性、时变、不确定等特性,因此一般采用常规方法很难准确建立其数学模型.神经网络是一种很好的非线性数学模型,运用多输入多输出的小波神经网络对抄纸过程中水分定量控制进行数学建模,并在小波神经的嘲络训练过程中采用了两种训练方法,加快了训练速度.结果表明,小波神经网络输出值与实际模型输出值的误差在允许范围内,可以很好的逼近抄纸过程的数学模型.     

基于小波神经网络的通用多变量非线性系统辨识算法和应用

在分析小波函数对L2(R)空间的逼近原理的基础上,给出了仅使用尺度函数的神经网络模型和网络学习方法,使得用于逼近低通系统的小波基函数大大减少,并给出逼近的理论依据.提出的小波神经网络模型的学习为线性LS参数估计问题,具有通用性和易用性,并具有线性系统中线性LS参数估计的优良性质,保证了在训练数据受噪声污染时的网络模型的推广能力.理论分析、仿真实验和实际应用结果都说明该辨识方法具有好的辨识精度和推广能力.     

模糊小波神经网络的研究及其应用

针对小波神经网络训练时间较长且易陷入局部极小值的缺点,文章提出了将小波神经网络融合模糊算法的方法,并建立了模糊小波神经网络模型及其训练算法,给出了该模型在变频调速系统故障预测中的应用实例。应用结果表明,模糊小波神经网络提高了网络训练速度,达到了优良的函数逼近效果。     

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．     

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.     

Artificial wavelet neuro-fuzzy model based on parallel wavelet network and neural network

From the well-known advantages and valuable features of wavelets when used in neural network, two type of networks (i.e., SWNN and MWNN) have been proposed. These networks are single hidden layer network. Each neuron in the hidden layer is comprised of wavelet and sigmoidal activation functions. First model is derived from adding the outputs of wavelet and sigmoidal activation functions, while in the second model outputs of wavelet and sigmoidal activation function are multiplied together. Using these proposed networks in consequent part of the neuro-fuzzy model, which result summation wavelet neuro-fuzzy and multiplication wavelet neuro-fuzzy models, are also proposed. Different types of wavelet function are tested with proposed networks and fuzzy models on four different types of examples. Convergence of the learning process is also guaranteed by performing stability analysis using Lyapunov function.     

Wavelet networks for nonlinear system modeling

This study presents a nonlinear systems and function learning by using wavelet network. Wavelet networks are as neural network for training and structural approach. But, training algorithms of wavelet networks is required a smaller number of iterations when the compared with neural networks. Gaussian-based mother wavelet function is used as an activation function. Wavelet networks have three main parameters; dilation, translation, and connection parameters (weights). Initial values of these parameters are randomly selected. They are optimized during training (learning) phase. Because of random selection of all initial values, it may not be suitable for process modeling. Because wavelet functions are rapidly vanishing functions. For this reason heuristic procedure has been used. In this study serial-parallel identification model has been applied to system modeling. This structure does not utilize feedback. Real system outputs have been exercised for prediction of the future system outputs. So that stability and approximation of the network is guaranteed. Gradient methods have been applied for parameters updating with momentum term. Quadratic cost function is used for error minimization. Three example problems have been examined in the simulation. They are static nonlinear functions and discrete dynamic nonlinear system.     

An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit

This paper presents an expert system based on wavelet decomposition and neural network for modeling and simulation of Chua’s circuit which is used for chaos studies. The problems which arise in modeling Chua’s circuit by neural networks are high structural complexity and slow and difficult training. With this proposed method a new solutions is produced to solve these problems. Wavelet decomposition is used for new useful feature extracting from input signal and neural network is used for modeling. Test results of proposed wavelet decomposition and neural network model are compared with test results of neural network model. Desired performance is provided by this new model. Test results showed that the suggested method can be used efficiently for modeling nonlinear dynamical systems.     

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).     

基于Java的发动机紧致型小波神经网络故障诊断

鉴于发动机是一种复杂的机电液一体化设备,其故障现象和原因之间存在复杂的非线性关联。本文结合小波变换的良好时频域特性和神经网络良好的非线性映射的优势,将MexicanHat小波基作为神经网络的传递函数,组建紧致型小波神经网络,用于发动机的故障诊断;本文以小波神经网络为算法基础,应用具有跨平台、可移植优点的Java语言和SQL Server2005数据库,开发出发动机智能故障诊断软件。     

Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network

Monitoring system for induction motor is widely developed to detect the incipient fault. Such system is desirable to detect the fault at the running condition to avoid the motor stop running suddenly. In this paper, a new method for detection system is proposed that emphasizes the fault occurrences as temporary short circuit in induction motor winding. The investigation of fault detection is focused on the transient phenomena during starting and ending points of temporary short circuit. The proposed system utilizes the wavelet transform for processing the motor current signal. Energy level of high frequency signal from wavelet transform is used as the input variable of neural network which works as detection system. Three types of neural networks are developed and evaluated including feed forward neural network (FFNN), Elman neural network (ELMNN) and radial basis functions neural network (RBFNN). The results show that ELMNN is the most simply and accurate system that can recognize all of unseen data test. Laboratory based experimental setup is performed to provide real-time measurement data for this research.     

Wavelet-based image interpolation using multilayer perceptrons

Changing the resolution of digital images and video is needed image processing systems. In this paper, we present nonlinear interpolation schemes for still image resolution enhancement. The proposed neural network interpolation method is based on wavelet reconstruction. With the wavelet decomposition, the image signals can be divided into several time–frequency portions. In this work, the wavelet decomposition signal is used to train the neural networks. The pixels in the low-resolution image are used as the input signal of the neural network to estimate all the wavelet sub-images of the corresponding high-resolution image. The image of increased resolution is finally produced by the synthesis procedure of wavelet transform. In the simulation, the proposed method obtains much better performance than other traditional methods. Moreover, the easy implementation and high flexibility of the proposed algorithm also make it applicable to various other related problems.     

Wavelet entropy and neural network for text-independent speaker identification

In the present study, the techniques of wavelet transform (WT) and neural network were developed for speech based text-independent speaker identification. The first five formants in conjunction with the Shannon entropy of wavelet packet (WP) upon level four features extraction method was developed. Thirty-five features were fed to feed-forward backpropagation neural networks (FFPBNN) for classification. The functions of features extraction and classification are performed using the wavelet packet and formants neural networks (WPFNN) expert system. The declared results show that the proposed method can make an effectual analysis with average identification rates reaching 91.09. Two published methods were investigated for comparison. The best recognition rate selection obtained was for WPFNN. Discrete wavelet transform (DWT) was studied to improve the system robustness against the noise of −2 dB.     

基于小波网络和多模块网络的数字识别

本文研究一种新的数字识别方法,这种方法用小波神经网络抽取特征、用多模块结构神经网络作模式分类器。小波分解的函数近似能力和人工神经网络的学习能力结合起来形成的小波神经网络,有着良好的特征描述性能,可用作特征抽取工具。多模块结构的神经网络将一个k类的模式分类问题转换为k个互相独立的2类分类问题。这种结构将一个复杂的分类问题化解为多个简单的分类问题,各个模块互相并联,各自负责一种模式的识别。用这种修改过的多模块结构网络的BP训练方法,可加速训练和提高训练精度,并且各模块可互相独立地进行训练。用美国NIST数字样本进行训练及测试,结果良好。这种方法可用于更广泛的平面图形识别。