Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter

2006 ◽  
Author(s):  
Huaiqi Kang ◽  
Caicheng Shi ◽  
Peikun He ◽  
Baojun Zhao
Keyword(s):  
Function Approximation ◽  
Learning Algorithm ◽  
Sequential Learning ◽  
Strong Tracking Filter ◽  
Tracking Filter

A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks

1997 ◽  
Vol 9 (2) ◽  
pp. 461-478 ◽  
Author(s):  
Lu Yingwei ◽  
N. Sundararajan ◽  
P. Saratchandran
Keyword(s):  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Sequential Learning ◽  
Benchmark Problems ◽  
Relative Contribution ◽  
Radial Basis

This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

scholarly journals High‐Order Extended Strong Tracking Filter

2021 ◽  
Vol 30 (6) ◽  
pp. 1152-1158
Author(s):  
SUN Xiaohui ◽  
WEN Tao ◽  
WEN Chenglin ◽  
CHENG Xingshuo ◽  
WU Yunkai
Keyword(s):  
High Order ◽  
Strong Tracking Filter ◽  
Tracking Filter
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