Externally Growing Cell Structures for Data Evaluation of Chemical Gas Sensors

Abstract

Based on Fritzke's GCS (Growing Cell Structures), we present here a new incremental self-organising neural network, the Externally Growing Cell Structures (EGCS). Our goals are to speed up the convergence and to improve the generalisation performance. The mechanism of internally growing cells in EGCS is the same as in GCS. However, when the Maximum Resource Vertex (MRV) or the Maximum Error Vertex (MEV) is a boundary node, the new cell is grown externally. Simulation results on neural network benchmarks, two-spiral problem and sonar mine/rock separation, indicate that EGCS performs better than the original GCS, measured by classification rate and the required number of epochs. As a new classification and regression method, the EGCS for Data Evaluation of Chemical Gas Sensors is introduced.