Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

MOTIVATION Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-...

Data classification with multilayer perceptrons using a generalized error function

The learning process of a multilayer perceptron requires the optimization of an error function E(y,t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E(Exp), inspired by the Z-EDM algorithm that we have recently proposed. An important property of ...

Robotic Manipulators Fault Diagnosis by Multilayer Perceptrons

In this paper a novel the artificial neural networks are used for both residual generation and residual analysis for fault diagnosis of robust manipulators. A Multilayer Perception (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so-called residual vector. The residuals, when properly ...

Model following using multilayer perceptrons

Functional preprocessing for multilayer perceptrons

In many applications, high dimensional input data can be considered as sampled functions. We show in this paper how to use this prior knowledge to implement functional preprocessings that allow to consistently reduce the dimension of the data even when they have missing values. Preprocessed functions are then handled by a numerical MLP which approximates the theoretical functional MLP. A succes...