87 research outputs found
3-D Velocity Regulation for Nonholonomic Source Seeking Without Position Measurement
We consider a three-dimensional problem of steering a nonholonomic vehicle to
seek an unknown source of a spatially distributed signal field without any
position measurement. In the literature, there exists an extremum seeking-based
strategy under a constant forward velocity and tunable pitch and yaw
velocities. Obviously, the vehicle with a constant forward velocity may exhibit
certain overshoots in the seeking process and can not slow down even it
approaches the source. To resolve this undesired behavior, this paper proposes
a regulation strategy for the forward velocity along with the pitch and yaw
velocities. Under such a strategy, the vehicle slows down near the source and
stays within a small area as if it comes to a full stop, and controllers for
angular velocities become succinct. We prove the local exponential convergence
via the averaging technique. Finally, the theoretical results are illustrated
with simulations.Comment: submitted to IEEE TCST;12 pages, 10 figure
Asymptotic equilibrium and stability of fuzzy differential equations
AbstractThe local existence and uniqueness theorems and the global existence of solutions were investigated in [1β3], respectively, for the Cauchy problem of fuzzy-valued functions of a real variable whose values are in the fuzzy number space (En, D). In this paper, we first study the asymptotic equilibrium for fuzzy evolution equations. Then, the stability properties of the trivial fuzzy solution of the perturbed semilinear fuzzy evolution equations are investigated by extending the Lyapunov's direct method
K
The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results
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