Pattern recognition using constructive algorithms

This paper aims to analyze the advantages of using constructive algorithms to achieve a functional neural network topology automatically. A constructive neural network simulator, named Kipu, was implemented in order to accomplish the design and analysis of constructive algorithms. Four constructive algorithms were modified to allow the use of real-valued data. Experiments using three pattern recognition tasks were conducted in the Kipu simulator and the results suggest a better functioning of the constructive algorithms compared with the fixed topology MLP network trained with backpropagation algorithm