Kalman Filtering And Neural Networks Pdf

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• Kalman filtering and neural networks
• Training Neural Networks for classification using the Extended Kalman Filter: A comparative study
• Kalman Filtering and Neural Networks (eBook, PDF)

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.

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Kalman filtering and neural networks

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.

Rent this article via DeepDyve. Hinton, G. Ciresan, D. Neural Networks , , vol. Singhal, S. Google Scholar. Haykin, S. Arasaratnam, I. Puskorius, G. Li, S. Chernodub, A. Mirikitani, D. Wan, E. Prokhorov, D. Cernansky, M. Bourlard, H. Golik, P. Nabney, I. Bishop, C. Download references. Correspondence to A. Neural Networks 23, 96— Download citation.

Received : 09 February Accepted : 04 April Published : 05 July Issue Date : April Search SpringerLink Search. Abstract Feedforward Neural Networks training for classification problem is considered. References 1. Chernodub Authors A. Chernodub View author publications. About this article. Cite this article Chernodub, A.

Training Neural Networks for classification using the Extended Kalman Filter: A comparative study

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. Rent this article via DeepDyve.

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace. Back, A.

Kalman Filtering and Neural Networks (eBook, PDF)

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Complexity

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