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Cleveland State University

2002

Kalman filtering

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Full-Text Articles in Engineering

Sum Normal Optimization Of Fuzzy Membership Functions, Daniel J. Simon Aug 2002

Sum Normal Optimization Of Fuzzy Membership Functions, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This …


Kalman Filtering With State Equality Constraints, Daniel J. Simon, Tien Li Chia Jan 2002

Kalman Filtering With State Equality Constraints, Daniel J. Simon, Tien Li Chia

Electrical and Computer Engineering Faculty Publications

Kalman filters are commonly used to estimate the states of a dynamic system. However, in the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically. For instance, constraints on state values (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. A rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed. The constraints may be time varying. At each time step the unconstrained Kalman filter solution is projected onto the state …