基于函数型连接神经网络的瓦斯传感器非线性校正

通过对载体催化瓦斯传感器检测原理的分析,指出瓦斯体积分数与瓦斯传感器输出电压之间呈非线性关系,提出了应用函数型连接神经网络的强非线性逼近能力,且不依赖确定的数学模型的优点,建立非线性校正模型,消除瓦斯检测中的非线性误差.网络仿真结果与分段线性拟合曲线的比较表明:这种非线性校正模型结构简单、收敛速度快、逼近精度高.     

一种基于LS-SVM构造FLANN的热电偶非线性校正方法*

提出一种基于最小二乘支持向量机（LS-SVM）构造函数链接型神经网络（FLANN）的方法,并根据正反馈原理将该FLANN应用於热电偶传感器非线性校正.讨论LS-SVM构造FLANN的基本原理和具体算法,给出了非线性补偿器的数学模型.与常规BP迭代算法构造的FLANN比较,该方法构造的FLANN补偿器具有如下优点：①利用LS-SVM将迭代逼近问题转化为直接求解多元线性方程,因此具有更快的速度;②整个训练过程中有且仅有一个全局极值点,确定了所构造FLANN补偿器的唯一性,提高了补偿精度.最后以Pt-Rh30-Pt-Rh6热电偶（B型）为例进行非线性校正实验,结果验证了上述结论.     

一种基于LS-SVM构造FLANN的热电偶非线性校正方法*

提出一种基于最小二乘支持向量机(LS-SVM)构造函数链接型神经网络(FLANN)的方法,并根据正反馈原理将该FLANN应用於热电偶传感器非线性校正.讨论LS-SVM构造FLANN的基本原理和具体算法,给出了非线性补偿器的数学模型.与常规BP迭代算法构造的FLANN比较,该方法构造的FLANN补偿器具有如下优点:①利用LS-SVM将迭代逼近问题转化为直接求解多元线性方程,因此具有更快的速度;②整个训练过程中有且仅有一个全局极值点,确定了所构造FLANN补偿器的唯一性,提高了补偿精度.最后以Pt-Rh30-Pt-Rh6热电偶(B型)为例进行非线性校正实验,结果验证了上述结论.     

基于模糊CMAC神经网络的热电偶非线性误差补偿研究

介绍了一种用单输入单输出模糊小脑神经网络(SISO FCMAC)对热电偶进行非线性误差补偿的方法。并以单片机89C5l以及外围芯片为核心设计了一个智能热电偶温度测试仪，给出了校正算法和硬件电路。     

RBF-PSO在N型热电偶非线性校正中的应用

为了解决大样本数据情况下N型热电偶应用中存在的问题,本文提出了基于粒子群算法优化径向基神经网络(RBF-PSO)进行热电偶非线性校正的新方法.仿真结果表明,采用该方法比以往采用的BP网络和RBF网络方法具有更高的拟合精度;同时以阳极焙烧过程中燃烧室温度为对象进行了仿真和实际应用研究,取得了满意的结果.     

基于RBF神经网络的热电偶信号处理新方法

针对热电偶信号处理中的非线性校正和冷端补偿等突出问题,利用径向基函数(RBF)神经网络构造双输入单输出的网络模型,并采用遗传算法对网络结构和参数进行优化训练,同时完成了热电偶测温中的非线性校正和冷端补偿。经仿真实验证明:该方法的测量误差减小至0.095%,在较大范围内提高了热电偶温度测量的精度。     

基于BP神经网络的非线性函数拟合

该文介绍了神经网络领域中BP神经网络的特点及其算法原理,对BP网络权值的修正规则进行了推导,并对BP网络存在的问题提出了几点改进,在此基础上对一组非线性函数的采样数据进行拟合。实验结果表明,BP神经网络能保证拟合误差在很小的范围之内,说明了其拟合的有效性。     

一种适用于微机型温度仪表的热电偶非线性校正

以热电偶为测温元件的微机型仪表,非线性校正是一个重要问题。采用热电势的逐点查表法,占有内容太多;采用直线或抛物线的分段近似法,一般需要采用浮点数计算,计算所需时间偏长。本文提出一种简化增量分度表法,它具有计算时间短、编程容易、精度高等优点。     

一种热电偶非线性校正的新方法

提出了对热电偶低温段非线性校正的一种新方法，通过对热电阻进行变换，取出电压信号，经过匹配参数及零点迁移后，将热电偶的凹曲线与热电阻的凸曲线合成，迭加，曲线被校值，效果颇佳。     

基于神经网络的非线性系统预测函数控制

针对离散非线性系统,利用神经网络非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络预测函数控制方法并给出了控制律的收敛性分析.该方法将复杂的神经网络非线性预测方程转化成直观而有效的线性形式,同时利用线性预测函数方法求得解析的控制律,避免了复杂的非线性优化求解,仿真结果表明了算法的有效性.     

Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network

A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification.     

基于神经网络的热电偶特性数学模型

简单介绍了当前热电偶应用存在的问题 ,分析了建立热电偶特性的数学模型的各种方法 ,并提出了应用前向多层神经网络建立热电偶特性数学模型的方法及其优势。文中对前向多层神经网络及其算法进行了深入剖析 ,并给出了MATLAB源代码。最后以镍铬 -镍硅热电偶为例 ,阐述了其特性数学模型的建立过程及其应用。     

Improved functional link artificial neural network via convex combination for nonlinear active noise control

A method relying on the convex combination of two normalized filtered-s least mean square algorithms (CNFSLMS) is presented for nonlinear active noise control (ANC) systems with a linear secondary path (LSP) and nonlinear secondary path (NSP) in this paper. The proposed CNFSLMS algorithm-based functional link artificial neural network (FLANN) filter, aiming to overcome the compromise between convergence speed and steady state mean square error of the NFSLMS algorithm, offers both fast convergence rate and low steady state error. Furthermore, by replacing the sigmoid function with the modified Versorial function, the modified CNFSLMS (MCNFSLMS) algorithm with low computational complexity is also presented. Experimental results illustrate that the combination scheme can behave as well as the best component and even better. Moreover, the MCNFSLMS algorithm requires less computational complexity than the CNFSLMS while keeping the same filtering performance.     

基于人工神经网络的动态过程聚类研究

为了有效解决传统人工神经网络对于时变函数的聚类问题,以及提高在大样本下网络的学习和泛化能力,提出了基于离散余弦变换的传统人工神经网络动态过程聚类方法。通过离散余弦变换将样本函数降维映射到由对应余弦参数所张成的模式特征空间,满足了传统人工神经网络对输入样本的要求,使传统人工神经网络实现动态过程的聚类成为可能。给出了实现算法,分析了计算复杂度,并使用基本竞争型人工神经网络对特征样本向量进行聚类,实验结果表明该方法是正确、有效的。与过程人工神经网络相比,该方法具有运算简单、物理意义明确等优点。     

A comparison between functional networks and artificial neural networks for the prediction of fishing catches

In recent years, functional networks have emerged as an extension of artificial neural networks (ANNs). In this article, we apply both network techniques to predict the catches of the Prionace Glauca (a class of shark) and the Katsowonus Pelamis (a variety of tuna, more commonly known as the Skipjack). We have developed an application that will help reduce the search time for good fishing zones and thereby increase the fleets competitivity. Our results show that, thanks to their superior learning and generalisation capacities, functional networks are more efficient than ANNs. Our data proceeds from remote sensors. Their spectral signatures allow us to calculate products that are useful for ecological modelling. After an initial phase of digital image processing, we created a database that provides all the necessary patterns to train both network types.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

This paper presents a computationally efficient nonlinear adaptive filter by a pipelined functional link artificial decision feedback recurrent neural network (PFLADFRNN) for the design of a nonlinear channel equalizer. It aims to reduce computational burden and improve nonlinear processing capabilities of the functional link artificial recurrent neural network (FLANN). The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity. Since it is a module nesting architecture comprising a number of modules that are interconnected in a chained form, its performance can be further improved. Moreover, the equalizer with a decision feedback recurrent structure overcomes the unstableness thanks to its nature of infinite impulse response structure. Finally, the performance of the PFLADFRNN modules is evaluated by a modified real-time recurrent learning algorithm via extensive simulations for different linear and nonlinear channel models in digital communication systems. The comparisons of multilayer perceptron, FLANN and reduced decision feedback FLANN equalizers have clearly indicated the convergence rate, bit error rate, steady-state error and computational complexity, respectively, for nonlinear channel equalization.