Kalman Filtering and Neural Networks | Semantic ScholarSkip to search form Skip to main content. Engineering Published DOI: This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control. View via Publisher. Alternate Sources.
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction
Rights meural permissions Reprints and Permissions? During the training phase, see Figs. Open in a separate window. Y with SP: the actual position with slip prediction in Y-axis.
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Understanding Kalman Filters, Part 1: Why Use Kalman Filters?
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.
X no SP: the actual position without slip prediction in X-axis. Skickas inom vardagar. The simulation of trajectory tracking is presented and discussed in Section 5. An ellipse Fig 8S4 Table trajectories are tracked under the high-level white noise.
The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model? Bloggat om Kalman Filtering and Neural Networks. This simplification introduces many zeros into the matrix Pk.