701 research outputs found
On Multi-Step Sensor Scheduling via Convex Optimization
Effective sensor scheduling requires the consideration of long-term effects
and thus optimization over long time horizons. Determining the optimal sensor
schedule, however, is equivalent to solving a binary integer program, which is
computationally demanding for long time horizons and many sensors. For linear
Gaussian systems, two efficient multi-step sensor scheduling approaches are
proposed in this paper. The first approach determines approximate but close to
optimal sensor schedules via convex optimization. The second approach combines
convex optimization with a \BB search for efficiently determining the optimal
sensor schedule.Comment: 6 pages, appeared in the proceedings of the 2nd International
Workshop on Cognitive Information Processing (CIP), Elba, Italy, June 201
Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization
One obstacle that so far prevents the introduction of machine learning models
primarily in critical areas is the lack of explainability. In this work, a
practicable approach of gaining explainability of deep artificial neural
networks (NN) using an interpretable surrogate model based on decision trees is
presented. Simply fitting a decision tree to a trained NN usually leads to
unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal
regularization during training, however, preserves the accuracy of the NN,
while it can be closely approximated by small decision trees. Tests with
different data sets confirm that L1-orthogonal regularization yields models of
lower complexity and at the same time higher fidelity compared to other
regularizers.Comment: 8 pages, 18th IEEE International Conference on Machine Learning and
Applications (ICMLA) 201
Kalman-Bucy-Informed Neural Network for System Identification
Identifying parameters in a system of nonlinear, ordinary differential
equations is vital for designing a robust controller. However, if the system is
stochastic in its nature or if only noisy measurements are available, standard
optimization algorithms for system identification usually fail. We present a
new approach that combines the recent advances in physics-informed neural
networks and the well-known achievements of Kalman filters in order to find
parameters in a continuous-time system with noisy measurements. In doing so,
our approach allows estimating the parameters together with the mean value and
covariance matrix of the system's state vector. We show that the method works
for complex systems by identifying the parameters of a double pendulum.Comment: 6 pages, 5 figures, Conference on Decision and Control 202
Gaussian Mixture Reduction via Clustering
Recursive processing of Gaussian mixture functions inevitably leads to a large number of mixture components. In order to keep the computational complexity at a feasible level, the number of their components has to be reduced periodically. There already exists a variety of algorithms for this purpose, bottom-up and top-down approaches, methods that take the global structure of the mixture into account or that work locally and consider few mixture components at the same time. The mixture reduction algorithm presented in this paper can be categorized as global top-down approach. It takes a clustering algorithm originating from the field of theoretical computer science and adapts it for the problem of Gaussian mixture reduction. The achieved results are on the same scale as the results of the current “state-of-the-art” algorithm PGMR, but, depending on the input size, the whole procedure performs significantly faster
Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation
In this paper, a Gaussian filter for nonlinear Bayesian estimation is introduced that is based on a deterministic sample selection scheme. For an effective sample selection, a parametric density function representation of the sample points is employed, which allows approximating the cumulative distribution function of the prior Gaussian density. The computationally demanding parts of the optimization problem formulated for approximation are carried out off-line for obtaining an efficient filter, whose estimation quality can be altered by adjusting the number of used sample points. The improved performance of the proposed Gaussian filter compared to the well-known unscented Kalman fiter is demonstrated by means of two examples
Gaussian Filtering using State Decomposition Methods
State estimation for nonlinear systems generally requires approximations of the system or the probability densities, as the occurring prediction and filtering equations cannot be solved in closed form. For instance, Linear Regression Kalman Filters like the Unscented Kalman Filter or the considered Gaussian Filter propagate a small set of sample points through the system to approximate the posterior mean and covariance matrix. To reduce the number of sample points, special structures of the system and measurement equation can be taken into account. In this paper, two principles of system decomposition are considered and applied to the Gaussian Filter. One principle exploits that only a part of the state vector is directly observed by the measurement. The second principle separates the system equations into linear and nonlinear parts in order to merely approximate the nonlinear part of the state. The benefits of both decompositions are demonstrated on a real-world example
On Sensor Scheduling in Case of Unreliable Communication
This paper deals with the linear discrete-time sensor scheduling problem in unreliable communication networks. In case of the common assumption of an error-free communication, the sensor scheduling problem, where one sensor from a sensor network is selected for measuring at a specific time instant so that the estimation errors are minimized, can be solved off-line by extensive tree search. For the more realistic scenario, where communication is unreliable, a scheduling approach using a prioritization list for the sensors is proposed that leads to a minimization of the estimation error by selecting the most beneficial sensor on-line. To lower the computational demand for the priority list calculation, a novel optimal pruning approach is introduced
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