An Introduction to the Kalman Filter
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Citations
Constrained dynamic parameter estimation using the Extended Kalman Filter
Enhanced localization for team robot navigation using compass sensor and USN
Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models
Design and implementation of a model predictive observer for AHRS
Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for Dynamic Expensive Multi-Objective Optimisation Problems
References
Stochastic Processes and Filtering Theory
Applied Optimal Estimation
Linear Optimal Control Systems
Related Papers (5)
Frequently Asked Questions (2)
Q2. What have the authors stated for future works in "Design and rule base reduction of a fuzzy ®lter for the estimation of motor currents" ?
The fuzzy estimator o ers the possibility of training if a nominal current history is known a priori. Further work on the topic of this paper is focusing on optimization methods that do better at ®nding the global minimum ( e. g., genetic algorithms ), integration of the ®ltering scheme with motor control, and real time implementation issues. It is not di cult to program a general purpose rule base reduction algorithm if the authors can make the following assumptions: ( 1 ) There are an odd number of membership functions for the two inputs and the output ; ( 2 ) the membership functions are symmetric triangles ; and ( 3 ) they desire to keep the two largest singular values in the R matrix of Eq. ( 56 ). A MATLAB m-®le for rule base reduction ( based on the algorithms presented in [ 10 ] and summarized here ) of a general two-input, oneoutput fuzzy logic system can be downloaded from http: //csaxp. csuohio. edu/ simon/reduce/.