Data-fusion, self-organizing continuous maps, and eigenflames applied to modeling, control and visualization

In this thesis a new Data-Fusion framework is presented. An object-oriented approach to neural network programming is considered, and the approach is used to develop the Self-Organizing Continuous Map, a family of neural networks based upon the self-organizing feature map. eigenflame and tomographic techniques are used for gas-turbine flame analysis.