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Mishra, Agya
- Higher Order Neural Networks Learning by Extended Kalman Filter
Abstract Views :157 |
PDF Views:3
Authors
Affiliations
1 Jabalpur Engineering College, Jabalpur, IN
2 Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, IN
3 Bhopal, IN
1 Jabalpur Engineering College, Jabalpur, IN
2 Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, IN
3 Bhopal, IN
Source
Digital Signal Processing, Vol 3, No 1 (2011), Pagination: 1-7Abstract
The Extended Kalman filter (EKF) is well known as a state estimation method for a nonlinear system and has been used to train a multilayered neural network (MNN) by augmenting the state with unknown connecting weights. The EKF Neural networks training algorithm is superior to standard back-propagation algorithm, but it is also known that Higher Order Neural Networks (HONN) have better performance than a standard multilayer perceptron networks. In this paper, more robust new learning algorithm for a Higher Order Neural Networks (HONN) based on EKF is proposed. The algorithm is an EKF coupled with HONN model, and has a new algorithm used to approximate the uncertainty of the system extreme nonlinearities. HONN consists of Generalized mean Neuron model (GMN).The GMN consists of an aggregation function which is based on generalized mean of all the inputs applied to it. EKF-HONN model is used for non linear state estimation, and to determine the weights and error covariance, the one step ahead prediction. Simulation results show that the proposed new algorithm is quite effective and can be implemented in the field of adaptive filtering nonlinear state estimation and predictions in place of traditional methods.Keywords
Higher Order Neural Networks, Generalized Mean-Neuron, Function Approximation, and Extended Kalman Filter, Nonlinear Filtering, Online Training.- Indoor RFID Tracking System Based on UKF Fusion Estimation Techniques
Abstract Views :167 |
PDF Views:5
Authors
Affiliations
1 Department of Electronics and Telecommunication Engineering, Jabalpur Engineering College (JEC), Jabalpur, Madhya Pradesh, 482011, IN
1 Department of Electronics and Telecommunication Engineering, Jabalpur Engineering College (JEC), Jabalpur, Madhya Pradesh, 482011, IN
Source
Digital Signal Processing, Vol 9, No 7 (2017), Pagination: 129-134Abstract
Radio Frequency Identification (RFID) is very popular and effective technology in the field of security and identification. In indoor RFID tracking system, the distance measured between the RFID reader and the tag is collected from the received signal strength indicator (RSSI). Due to multivariate, irregularly sampled, uncertain, and nonlinear of the measurements and limit of the deployment of readers, an efficient estimation method is involved to acquire the accurate trajectory in indoor RFID tracking system. In this paper, features of RFID reader measurement mechanism are analyzed, an RFID measurement system model is proposed, and the Unscented Kalman Filter (UKF)-based fusion estimation algorithm is proposed for real trajectory tracking. UKF have an ability to deal with nonlinearity of the system and adaptive to the uncertainty of the RFID measurement data. This paper concludes with experimental analysis of performance of the proposed system model. Results show that this model can be implemented efficiently in the field of trajectory tracking and personal identification system.Keywords
Adaptive Filtering, Augmented UKF, RFID Indoor Tracking, RFID Measurement Data, RSSI.References
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