R
Richard E. Mortensen
Researcher at University of California, Los Angeles
Publications - 6
Citations - 337
Richard E. Mortensen is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Stochastic differential equation & Filter (signal processing). The author has an hindex of 4, co-authored 6 publications receiving 329 citations.
Papers
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Journal ArticleDOI
Mathematical problems of modeling stochastic nonlinear dynamic systems
TL;DR: In this paper, the authors introduce the engineer to the area of stochastic differential equations, and point out the mathematical techniques and pitfalls in this area Topics discussed include continuous-time Markov processes, the Fokker-Planck-Kolmogorov equations, the Ito and Stratonovich stochastically calculi, and the problem of modeling physical systems.
Journal ArticleDOI
A globally stable linear attitude regulator
TL;DR: In this paper, the problem of designing an attitude regulator for an arbitrary rigid body was considered, and the design criterion was that the system should have only one equilibrium point, and this point should be asymptotically stable for arbitrary initial conditions.
Book
Random Signals and Systems
TL;DR: Discussion of Probability and Stochastic Processes The Gaussian Distribution in One and Two Dimensions and Finite Random Sequences and Discrete-time Kalman Filtering.
Journal ArticleDOI
A Priori Open Loop Optimal Control of Continuous Time Stochastic Systems
TL;DR: In this paper, the problem of determining the optimal open loop control, when no observations at all are available, is treated by dynamic programming and a quasi-linearization type of algorithm for its solution is presented.
Proceedings ArticleDOI
Balakrishnan's white noise model versus the Wiener process model
TL;DR: In this article, an example is considered in which the Ito formalism appears to be lacking in merit when the issue of performing actual numerical computation is introduced and an alternative formalism, called the Balakrishnan white noise model, which is claimed to exhibit more merit in the given example.