3D Nonparametric Neural Identification

Nonparametric Neural Networks

Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks from scratch. In this paper, we address the problem of automatically finding a good network size during a single training cycle. We introduce nonparametric neural networks, a non-probabilistic framework for conducting ...

Nonparametric System Identification

This article presents a survey of various methods for nonparametric identification of nonlinear systems. Nonparametric identification methods are those that measure Wiener kernels or Volterra kernels, since an output of a nonlinear system can be described by the convolution integral of Wiener or Volterra kernels and the system input. Section 1 highlights the representation methods of nonlinear ...

NONPARAMETRIC IDENTIFICATION Rosa

Nonparametric Identification of Copula Structures

We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process. We perform simulation experiments to evaluate our test and conclude that our ...

Nonparametric Bayesian Models for Neural

of “Nonparametric Bayesian Models for Neural Data” by Frank Wood, Ph.D., Brown University, May 2007. Many neural data analyses can be cast as latent variable modeling problems. Specific examples include spike sorting and neurological data analysis. Challenges in spike sorting include figuring out how many neurons generated a set of recorded action potentials and, further, which neuron generated...