Composition is a powerful and simple approach for obtaining numerical integration methods of high accuracy order while preserving the geometric properties basic integrator. Adaptive step size control allows one to significantly increase performance methods. However, there lack efficient algorithms composition solvers due some known difficulties in constructing low-cost embedded local error esti...

In this paper, a fast adaptive neural regression estimator named FANRE is proposed. FANRE exploits the advantages of both Adaptive Resonance Theory and Field Theory while contraposing the characteristic of regression problems. It achieves not only impressive approximating results but also fast learning speed. Besides, FANRE has incremental learning ability. When new instances are fed, it does n...

This paper proposes a switching adaptive control for trajectory tracking of unmanned aircraft systems. The switching adaptive control method is designed to overcome the wind disturbance and achieve a proper tracking performance for control systems. In the suggested system, the wind disturbance is regarded as a finite set of uncertainties; a controller is designed for each uncertainty, and a per...

We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding es...

This paper deals with a projection least squares estimator of the drift function jump diffusion process X computed from multiple independent copies observed on [0, T]. Risk bounds are established this and an associated adaptive estimator. Finally, some numerical experiments provided.

The problem of estimation of the nite dimensional parameter in a partial linear model is considered. We derive upper and lower bounds for the second minimax order risk and show that the second order minimax estimator is a penalized maximum likelihood estimator. It is well known that the performance of the estimator is depending on the choice of a smoothing parameter. We propose a practically fe...

We propose a procedure to handle the problem of Gaussian regression when the variance is unknown. We mix least-squares estimators from various models according to a procedure inspired by that of Leung and Barron [IEEE Trans. Inform. Theory 52 (2006) 3396–3410]. We show that in some cases, the resulting estimator is a simple shrinkage estimator. We then apply this procedure to perform adaptive e...