Lyapunov exponents of a dynamical system give information about its long-term evolution. Exponents estimation is not an easy task; it is computationally costly and, in presence of chaotic dynamics, it exhibits numerical difficulties. In a previous paper, the authors proposed an algorithm for Lyapunov exponents estimation in piecewise linear systems that strongly reduces the computational time. ...

Inspired by the theory of multiresolution analysis (MRA) of wavelets and artificial neural networks, a multiresolution neural network (MRNN) for approximating arbitrary nonlinear functions is proposed in this paper. MRNN consists of a scaling function neural network (SNN) and a set of sub-wavelet neural networks, in which each sub-neural network can capture the specific approximation behavior (...

It is well known that the adaptive algorithm is simple and easy to program but the results are not fully competitive with other nonlinear methods such as free knot spline approximation. We modify the algorithm to take full advantages of nonlinear approximation. The new algorithms have the same approximation order as other nonlinear methods, which is proved by characterizing their approximation ...

This paper introduces a new approach to the recursive propagation of probability density functions through discrete-time stochastic nonlinear dynamic systems. An efficient recursive procedure is proposed that is based on the optimal approximation of the posterior densities after each prediction step by means of Dirac mixtures. The parameters of the individual components are selected by systemat...

We compare different nonlinear approximations to gravitational clustering in the weakly nonlinear regime, using as a comparative statistic the evolution of non-Gaussianity which can be characterised by a set of numbers Sp describing connected moments of the density field at the lowest order in 〈δ2〉: 〈δ〉c ≃ Sn〈δ〉. Generalizing earlier work by Bernardeau (1992) we develop an ansatz to evaluate al...

We consider uncapacitated and capacitated assortment problems under the paired combinatorial logit model, where the goal is to find a set of products to maximize the expected revenue obtained from each customer. In the uncapacitated setting, we can offer any set of products, whereas in the capacitated setting, there is a limit on the number of products that we can offer. We establish that even ...