File Name: kalman filtering and neural networks .zip
This paper presents an identification method of dynamic systems based on a group method of data handling approach.
- A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model
- Kalman filtering and neural networks
- Kalman Filtering and Neural Networks
- Kalman Filtering and Neural Networks (eBook, PDF)
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Haykin Published Engineering. From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks.
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model
Feedforward Neural Networks training for classification problem is considered. The Extended Kalman Filter, which has been earlier used mostly for training Recurrent Neural Networks for prediction and control, is suggested as a learning algorithm. Implementation of the cross-entropy error function for mini-batch training is proposed. Popular benchmarks are used to compare the method with the gradient-descent, conjugate-gradients and the BFGS Broyden-Fletcher-Goldfarb-Shanno algorithm. The influence of mini-batch size on time and quality of training is investigated. This is a preview of subscription content, access via your institution.
Kalman filtering and neural networks
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model.
Kalman filter theory applied to the training and use of neural networks, and some applications of learning algorithms derived in this way. It is organized as.
Kalman Filtering and Neural Networks
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The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained.
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Kalman Filtering and Neural Networks (eBook, PDF)
Jetzt bewerten Jetzt bewerten. State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other …mehr. DE Als Download kaufen.
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