文件名称:hybridSIREKF
介绍说明--下载内容均来自于网络,请自行研究使用
To estimate the input-output mapping with inputs x
% and outputs y generated by the following nonlinear,
% nonstationary state space model:
% x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)]
% + 8cos(1.2t) + process noise
% y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3
% + time varying measurement noise
% using a multi-layer perceptron (MLP) and both the EKF and
% the hybrid importance-samping resampling (SIR) algorithm. -To estimate the input-output mapping with inputs x and outputs y generated by the following nonlinear, nonstationary state space model: x (t+ 1) = 0.5x (t)+ [25x (t )]/[( 1+ x (t)) ^ (2)]+ 8cos (1.2t)+ process noise y (t) = x (t) ^ (2)/20+ 6 squareWave (0.05 (t-1 ))+ 3+ time varying measurement noise using a multi-layer perceptron (MLP) and both the EKF and the hybrid importance-samping resampling (SIR) algorithm.
% and outputs y generated by the following nonlinear,
% nonstationary state space model:
% x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)]
% + 8cos(1.2t) + process noise
% y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3
% + time varying measurement noise
% using a multi-layer perceptron (MLP) and both the EKF and
% the hybrid importance-samping resampling (SIR) algorithm. -To estimate the input-output mapping with inputs x and outputs y generated by the following nonlinear, nonstationary state space model: x (t+ 1) = 0.5x (t)+ [25x (t )]/[( 1+ x (t)) ^ (2)]+ 8cos (1.2t)+ process noise y (t) = x (t) ^ (2)/20+ 6 squareWave (0.05 (t-1 ))+ 3+ time varying measurement noise using a multi-layer perceptron (MLP) and both the EKF and the hybrid importance-samping resampling (SIR) algorithm.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
demo2
.....\#sirdemo2.m#
.....\gradupdate.m
.....\hybridsir.m
.....\mlp.m
.....\mlpekf.m
.....\mlph.m
.....\mlpratios.m
.....\predictmlp.m
.....\resamplemlp.m
.....\sirdemo2.m
.....\#sirdemo2.m#
.....\gradupdate.m
.....\hybridsir.m
.....\mlp.m
.....\mlpekf.m
.....\mlph.m
.....\mlpratios.m
.....\predictmlp.m
.....\resamplemlp.m
.....\sirdemo2.m