基于神经网络的仪器非线性校准系统研究

探讨了采用神经网络校准仪器非线性的方法,并用递推预报误差算法训练神经网络.理论分析和实验结果表明,神经网络方法优于传统的最小二乘法,能够取得很好的效果.     

基于递推预报误差算法的前馈神经网络的设计

该文介绍了一种基于递推预报误差算法的前馈神经网络的实现方法。将该网络应用于非线性系统模型的仿真试验中取得了良好的效果。文中给出了试验的结果,并对该网络的应用进行了讨论。     

用神经网络建立非线性系统模型研究

本文针对多层网络结构，运用递推预报误差（ＲＰＥ）算法对离散非线性系统进行辨识研究，作为应用实例，本文对一个工业实际进行了神经网络动态建模，研究结果表明，神经网络方法是用于带有非线性特性工业过程建模的有效方法。     

语音动力学系统的神经网络建模方法研究

人工神经网络(ANN)方法是非线性动力学系统 建模的有效方法．本文针对多层ANN结构,运用递推预报误差(RPE)算法对离散非线性动力学 系统进行了建模研究,并将之运用于语音非线性动力学系统的动态建模,估计出了语音非线 性动力学系统稳态吸引子的维数,为了解语音和实用化的语音识别提供了良好的基础．     

小波分解及神经网络在海浪预报中的应用

提出采用小波分解与递推平方根法神经网络模型相结合的方法进行海浪预报。采用小波分析方法既能把握海浪的发展变化趋势,又能简化预报模型,同时基于递推平方根法的神经网络模型预报方法不仅收敛速度快,又能很好地提高精度,减少计算量。即先将不规则海浪信号用小波分析方法进行多尺度一维分解,得到相对简单、规则的准周期分量信号,然后用一种基于递推平方根法的神经网络模型对各重构信号进行预报,最后对预报结果进行集成。最终的仿真结果验证了该方法的有效性。     

船舶航向非线性系统鲁棒跟踪控制

对船舶航向非线性系统, 提出了一种基于神经网络方法的鲁棒跟踪控制器. 系统由船舶运动非线性响应模型和舵机伺服系统串联构成, 其中运动响应模型考虑了建模误差和外界干扰力等非匹配不确定性. 对建模误差和期望舵角的一阶导数项应用在线二层神经网络予以辨识和补偿, 不确定性干扰项处理应用L2增益设计. 采用Lyapunov函数递推法, 得到包括神经网络权值算法在内的跟踪控制器. 跟踪误差和神经网络权值误差的一致终值有界性保证了系统的鲁棒稳定性, 合理的控制器参数选择保证了控制精度. 仿真结果验证了控制器的有效性.     

基于小波神经网络的非线性系统建模研究

研究了用小波神经网络对非线性系统进行建模问题。提出了用带遗忘因子的最小二乘法训练网络的权值,利用递推预报误差算法训练伸缩因子和平移因子的交互辩识算法。仿真结果证明了算法的有效性。     

基于小波神经网络的非线性误差校正模型及其预测

针对非线性系统的预测问题,在线性和非线性协整理论涵义的基础上,提出利用小波神经网络进行非线性协整系统的非线性误差校正模型的研究,并给出该模型的建模方法．对沪深股市进行实证研究,与线性向量自回归模型进行比较．研究证明,小波神经网络所建立的非线性误差校正模型有较好的预测效果,能够有效地预测非线性经济系统．     

小型无人直升机反步自适应控制

针对具有强非线性、高度耦合以及参数不确定性特点的小型无人直升机系统,提出一种基于小脑模型关节控制器（Cerebellar Model Articulation Control,CMAC）神经网络的自适应反步控制方法,该方法采用小脑模型关节控制器神经网络在线学习系统不确定性以及反步控制中各阶虚拟控制量的导数信息,设计鲁棒控制项克服CMAC神经网络在线学习系统不确定性的误差,控制律由反步法回归递推得到。仿真结果表明,在模型参数不确定和存在较大误差的情况下,所设计的控制律具有理想的姿态跟踪性能以及良好的鲁棒性。     

改进RPE算法的神经网络在客户欺诈预测中的应用

客户欺诈在一定程度上抑制了消费，这会妨碍电信运营商和电信用户的亲密度，从而削弱电信运营商的市场竞争力。客户欺诈现象存在非常复杂的多元非线性关系，从统计学角度出发，难以建立预测模型，针对这些问题，提出了基于递推预报误差(RPE)算法神经网络的方法建模，并采用改进的动态遗忘因子方法保证了平稳收敛。实验结果表明，用该算法预测客户欺诈的危险度效果优于BP神经网络模型，具有实用性和有效性。     

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.     

一种基于小波神经网络故障检测方法的仿真研究

文中提出了一种基于小波神经网络一性观测器的故障检测方法。它是一种把信号分析和模型相结合的故障检测方法,通过小波对信号的去噪和神经的神经网络的自学习功能,来获取系统输入输出的非线性动力学特性,进而实时计算出残差并进行逻辑判疡,可提高故障检测的速度和准确率。对同步交流电机的结构损伤故障进行了仿真,结果表明了该方法是可行的。     

A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater

This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.     

基于分散神经网络的多指灵巧手位置控制

针对多指灵巧手钢缆传动系统的非线性，提出一种基于分散神经网络的位置控制方法．通过 对复杂的钢缆传动系统施加不同的输入可以得到特定的相对简单的输入输出数据，利用这种 特定的输入输出数据学习传动系统的非线性关系得到多个分散的神经网络，再根据传动系统 的结构特性用分散的神经网络求取钢缆传动系统的逆模型，用于直接逆控制，从而达到补偿 非线性误差的目的．同时应用在线神经网络的适时补偿使系统长时间保持良好的运行状态． 实验证明这种方法可大大提高位置跟踪精度，取得比较满意的结果．     

基于模块化模糊神经网络的非线性系统故障诊断

提出了一种基于模块化模糊神经网络的非线性系统故障诊断新方法。该方法先使用模糊c-均值聚类法对测量空间进行模块分割,再利用模糊IF－THEN规则对分割后的子空间分别采用局部BP模型进行逼近,最后,通过离线学习获得不同子空间故障输出与测量输入的非线性动力特性。试验表明该网络具有良好的泛化性能,可显著提高非线性系统故障检测的快速性、鲁棒性及准确率。     

On the computation of the direct kinematics of parallelmanipulators using polynomial networks

Polynomial learning networks are proposed in this paper to solve the forward kinematic problem for a planar three-degree-of-freedom parallel manipulator with revolute joints. These networks rapidly learn complex nonlinear functions based on a database mapping. The networks learn the forward kinematics of the manipulator based on examples of the transformation. The obtained networks are then used to follow a test trajectory. For comparison purposes, a neural network approach using backpropagation is also used for this problem. The results show that, in this application, polynomial networks learn much faster and exhibit less error than neural networks     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.     

Adaptive neural network control of flexible joint robots based on feedback linearization

A robust adaptive neural network controller is presented for flexible joint robots using feedback linearization techniques. The controller is based on an approach of using an additional neural network to provide adaptive enhancements to a bask fixed nonlinear controller which can be either neural-network-based or model-used. The weights of the additional neural network are updated on-line based on direct adaptive techniques. It is shown that if Gaussian radial basis function networks are used for the additional neural network, uniformly stable adaptation is assured and asymptotic tracking of the position reference signal is achieved. Intensive computer simulations on a two-link flexible joint robot have shown that the controller can belter handle dynamical model changes and parameter uncertainties than the conventional feedback linearization controller     

Representing knowledge by neural networks for qualitative analysisand reasoning

A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described     

基于人工神经网络铂电阻非线性估计方法的研究􀀁

本文提出了一种基于人工神经网络的铂电阻传感器非线性估计方法,该方法用二次幂级数多项式拟合温度传感器的非线性模型,多项式的系数可由神经网络学习算法得到,当条件发生变化时,只要给出几组测量数据对,通过该方法可自动重新训练网络,获得新的多项式系数,实现传感器的非线性估计。