Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

... Second, they can be characterized by 175 Kalman Filtering and Neural Networks, Edited by Simon Haykin ISBN 0-4 7 1-3 699 8-5 # 2001 John Wiley & Sons, Inc. Kalman Filtering and Neural Networks, ... smooth- ing (EKS), which infers the hidden state distribution during the E-step of our algorithm. The nonlinear model is linearized about the current state es...

... paper by Kalman [1] for the 1 Kalman Filtering and Neural Networks, Edited by Simon Haykin ISBN 0-4 7 1-3 699 8-5 # 2001 John Wiley & Sons, Inc. Kalman Filtering and Neural Networks, Edited by Simon ... Inc. ISBNs: 0-4 7 1-3 699 8-5 (Hardback); 0-4 7 1-2 215 4-6 (Electronic) With these two theorems at hand, the derivation of the Kalman filter f...

... 6-4 R-2R-1 5,000 25,000 1 0.69 1.04 Ikeda 6-6 R-5R-1 5,000 25,000 1 0.354 1.51 Lorenz 3-8 R-7R-1 5,000 25,000 40 0.040 2.09 NH 3 laser 9-1 0R-8R-1 1,000 9,000 1 a 0.147 2.01 Sea clutter 6-8 R-7R-1 ... Inc. Kalman Filtering and Neural Networks, Edited by Simon Haykin Copyright # 2001 John Wiley & Sons, Inc. ISBNs: 0-4 7 1-3 699 8-5 (Hardback); 0-4 7 1-2 215 4-...

... observations. 123 Kalman Filtering and Neural Networks, Edited by Simon Haykin ISBN 0-4 7 1-3 699 8-5 # 2001 John Wiley & Sons, Inc. Kalman Filtering and Neural Networks, Edited by Simon Haykin Copyright ... 514–519. [16] Z. Ghahramani and S.T. Roweis, ‘‘ Learning nonlinear dynamical systems using an EM algorithm, ’’ in Advances in Neural Information...

... pattern fell within the assigned region and 0:8 otherwise. Figure 7.10 illustrates the classification task, learning curves for the UKF and EKF, and the final classification regions. For the learning curve, ... smoother performance. In this case, the network models are trained on the clean time series, and then tested on the noisy data using the standard extended...

... P i k and the weight vectors w i k are independent of the chosen number of streams. On the other hand, we noted above the increase in size for the derivative matrices H i k: l , as well as of the Kalman ... diagonal value of 100 and 1,000 for weights corresponding to nonlinear and linear nodes, respectively. Then, the user of these methods must set values for the le...

... in the complexity of the learning task. However, since the number of weights in the network is limited and remains the same as in the other experiments, the network cannot simply memorize the ... order. Then, during testing, the order of the sequences was varied and the network was asked to predict the correct shape and location of the next image in the seq...