Externally growing cell structures for data evaluation of chemical gas sensors

Based on Fritzke's GCS (Growing Cell Structures), we present here a new inc remental self-organising neural network, the Externally Growing Cell Struct ures (EGCS). Our goals are to speed up the convergence and to improve the g eneralisation performance. The mechanism of internally growing cells in EGC S 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 e xternally. Simulation results on neural network benchmarks, two-spiral prob lem 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.