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Wavelet neural network (WNN) trained by unscented Kalman filter (UKF) has many merits of fast convergent rate and small prediction error without computing the Jacobian matrix. Based on this, an improved UKF is introduced into the parameters estimation for WNN. The algorithm uses an unscented transform (UT) based on minimal skew simplex Sigma point sampling strategy in the frame of Kalman filter, which...
Tracking agile aircraft under high accelerations generally demands sophisticated models for determining trajectories with desirable dynamics and accuracy. Often this raises complexity of the estimation algorithm as it gives rise to more elaborated methods for both taking model nonlinearities into account and handling a greater number of state variables that describe the model. The approach of this...
This paper presents a GPS positioning method based on neural network adaptive Kalman filter. Using the innovation vector which reflects the degree how the model fits the data, and real-timely accessing to the innovation vector's ratio of the theoretical variance to the actual of variance, we can get the working conditions of Kalman filter. Then track the change of system parameters through neural...
This paper presents a new method to estimate process noise covariances Q and observation noise covariances R of nonlinear time-varying system. Based on the analysis of key factor to estimate noise covariances Q and R for identification and prediction of nonlinear time-varying system using extended Kalman filter based on neural network. Then the paper extends the Mehra approach of noise statistics...
There are many practical situations in which the chaotic signal appears in the observation in a random manner so that there are intermittent failures in the observation mechanism at certain times. Using weights and network output of neural network as state equation and observation equation to obtain the linear state transition equation, and the chaotic time-series prediction results represented by...
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