A Comparative Evaluation of Constructive Neural Networks Methods using PRM and BCP as TLU Training Algorithms
João Roberto Bertini, Maria do Carmo Nicoletti, Estevam Rafael Hruschka Junior · 2006
Constructive neural network algorithms enable the architecture of a neural network to be constructed as an intrinsic part of the learning process. These algorithms are very dependent on the TLU training algorithm they employ. Generally they use a Perceptron-based algorithm (such as Pocket or Pocket with Ratchet Modification (PRM)) for training each individual node added to the network, during the learning process. In the literature can be found a vast selection of algorithms for training individual TLUs. This paper investigates the use of the Barycentric Correction Procedure (BCP) algorithm with four constructive algorithms namely Tower, Pyramid, Shift and Perceptron-Cascade. Results show that some constructive neural algorithms have better performance using BCP than using PRM.