共查询到18条相似文献,搜索用时 187 毫秒
1.
2.
3.
针对非线性动态系统控制问题,提出了一种基于过程神经网络的控制信号求解模型和算法。利用过程神经网络对动态系统时变输入/输出信号的非线性映射机制和对系统过程模态特征的自适应提取能力,建立基于过程神经网络的辨识模型;然后根据所建立的辨识模型、系统控制结构和状态参数之间的关系,构建可满足系统信息传递约束关系的控制信号求解模型。分析了过程神经网络控制模型的信息处理机制,给出了基于GA与LMS相结合的优化求解算法,实验结果验证了模型和算法的有效性。 相似文献
4.
5.
6.
7.
针对抽油机井示功图模式诊断问题,提出了一种基于过程神经元网络的动态诊断模型和方法。过程神经元网络(PNN)的输入和连接权均可以是时变函数,通过对训练函数样本集的学习,可自动抽取时变函数样本的过程模式特征,并可将多个过程特征加以组合形成类别输出,在机制上对时变信号的分类问题具有较好的适应性。建立了一种基于PNN的动态诊断模型和方法,给出了基于函数基展开结合梯度下降的学习算法,对油田实测的抽油机井示功图进行工作状态识别,取得了较好的应用效果。 相似文献
8.
通过对系统输入信号的设计,使Hammerstein系统输出只反映系统的线性动态,并将非线性部分的静态影响有效地分离掉.利用最小二乘辨识得到系统的线性动态模型.基于此模型并依据系统的测量输出重构系统的中间输入,进而可估计出非线性部分的参数,据此给出了多变量Hammerstein系统辨识的动态分离方法.仿真结果表明所提出的方法是有效的. 相似文献
9.
一类反馈过程神经元网络模型及其学校算法 总被引:9,自引:0,他引:9
提出了一种基于权函数基展开的反馈过程神经元网络模型.该模型为三层结构,由输入层、过程神经元隐层和过程神经元输出层组成.输入层完成系统时变过程信号的输入及隐层过程神经元输出信号向系统的反馈;过程神经元隐层用于完成输入信号的空间加权聚合和激励运算,同时将输出信号传输到输出层并加权反馈到输入层;输出层完成隐层输出信号的空间加权聚集和对时间的聚合运算以及系统输出.文中给出了学习算法,并以旋转机械故障自动诊断问题为例验证了模型和算法的有效性. 相似文献
10.
11.
针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性. 相似文献
12.
A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method. 相似文献
13.
14.
基于SVR的传感器Hammerstein模型辨识 总被引:1,自引:0,他引:1
提出一种基于支持向量回归机的非线性动态传感器Hammerstein模型辨识方法并给出了相关的数学理论及学习算法.在该模型中,用非线性静态子环节和线性动态子环节串联来描述传感器的非线性动态特性.再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型,并通过SVR求取中间模型参数.最后,推导出中间模型参数与传感器Hammerstein模型参数之间的关系,并由该关系实现非线性静态环节和线性动态环节的同时辨识.用实际力传感器动态标定实验数据进行测试,结果表明与常规非线性传感器辨识方法不同,所提方法只需进行一次动态标定实验就能给出非线性动态模型的数学解析表达式.且建立的力传感器Hammerstein模型阶次为4,而线性动态系统模型则需要6阶才能达到相同的精度.因此该研究为传感器非线性动态系统辨识又提供了一种可选方法. 相似文献
15.
Christoph Hametner Markus Stadlbauer Maxime Deregnaucourt Stefan Jakubek Thomas Winsel 《Engineering Applications of Artificial Intelligence》2013,26(1):251-261
This paper addresses the topic of model based design of experiments for the identification of nonlinear dynamic systems. Data driven modeling decisively depends on informative input and output data obtained from experiments. Design of experiments is targeted to generate informative data and to reduce the experimentation effort as much as possible. Furthermore, design of experiments has to comply with constraints on the system inputs and the system output, in order to prevent damage to the real system and to provide stable operational conditions during the experiment. For that purpose a model based approach is chosen for the optimization of excitation signals in this paper. Two different modeling architectures, namely multilayer perceptron networks and local model networks are chosen and the experiment design is based on the optimization of the Fisher information matrix of the associated model architecture. The paper presents and discusses feasible problem formulations and solution approaches for the constrained dynamic design of experiments. In this context the effects of the Fisher information matrix in the static and the dynamic configurations are discussed. The effectiveness of the proposed method is demonstrated on a complex nonlinear dynamic engine simulation model and an analysis as well as a comparison of the presented model architectures for model based experiment design is given. 相似文献
16.
Intelligent diagnosis method for a centrifugal pump using features of vibration signals 总被引:1,自引:0,他引:1
In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because
the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because
definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis
method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived
using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used
to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency
regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics
of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using
the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition
diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically.
The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical
examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method. 相似文献
17.
A functional approach has been developed to represent continuous separable, nonlinear systems of a general type based on a modified Volterra series. The importance of this is that the effects of bias or mean signal level within the nonlinear system can be separated from dynamic effects. This has particular significance in the development of identification procedures based on cross-correlation functions, as these functions can now be estimated practically without any influence from the bias level. Practical identification of the gain characteristics for an electrohydraulic servo is described using three-level pseudorandom input signals which are cross-correlated with the sampled system response in a minicomputer to provide an automated procedure. Good accuracy is achieved even in the presence of severe noise. 相似文献
18.
Xuan HanAuthor VitaeWen-Fang XieAuthor Vitae Zhijun FuAuthor VitaeWeidong LuoAuthor Vitae 《Neurocomputing》2011,74(17):3428-3439
In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results. 相似文献