【作者】 彭金华；

【导师】 吴乐南；

【作者基本信息】 东南大学 ， 信号与信息处理， 2006， 硕士

【摘要】 本论文对神经网络理论用于最优非线性滤波进行了研究,主要完成了以下研究工作:分析了神经网络理论应用于最优非线性滤波的现状及发展趋势,并对神经网络和经典的最优非线性滤波方法进行简单的讨论,其中包括非线性最小方差(LMS)估计(即扩展的卡尔曼滤波)和非线性最小二乘估计(RLS),研究了神经网络应用于最优非线性滤波的可行性。探讨了基于反向传播(BP)网络和基于径向基函数(RBF)网络的最优非线性滤波,利用MATLAB作为仿真软件,并应用BP网络和RBF网络对实例进行了仿真,从实验的角度验证了验证了RBF网络在非线性滤波方面的优越性,它具有训练时间短、所用神经元数目少、精度高等突出优点。最后介绍了基于函数型神经网络的最优非线性滤波,给出了相应的仿真实验结果。可以看到,神经网络用于最优非线性滤波具有广阔的发展前景。更多还原

【Abstract】 This thesis explores the neural networks approach to optimum nonlinear feltering. The main research work is as follows:It analysis the actualities and the developing trends about the neural networks approach to optimum nonlinear filtering, itneural networks and the classical theory about the optimum nonlinearfiltering.Among them it is included not only the nonlinear Least Mean Square (LMS) estimation (spreaded Kalman filter), but the nonlinear Recursive Least-Square (RLS) estimation also.At the same time it analysis the feasibility of integration of neural networks and optimum nonlinear filtering.It discusses the Back Propagation (BP) network and the Radial Basis Function (RBF) network to optimum nonlinear filtering. With the MATLAB as software tools,simulations are made which applies the BP network and the Radial Basis Function (RBF) network to realism examples. A series of simulation waves are obtained, thus,the accuracy of the simulation model is validated by experiments.And the simulation verifies the superiority of the RBFs in nonlinear filtering such as shorter training time, least note and higher accuracy.Finally,the optimum nonlinear filtering based on Functional Link Artificial Neural Netwok (FLANN) is described,with its experimental results.It can be seen,the ANN used in the optimum nonlinear filtering may be a tendency in filtering of noise.更多还原