Application of Hierarchical Mixture of Experts Networks to Engine Knock Detection

This paper describes the application of a hierarchical modular neural network for an advanced approach solving the knock detection task for combustion engines. The knock detection is realized on a two-level feature extraction approach. It is not only based on the popular cycle-by-cycle classification but a tendency index for the knock condition is determined. The experimental environment consisted of several measurement sessions in a special test cell with an in-production car. A hierarchical mixture of experts network is used as neural classifier. Gradient-Basedand Expectation-Maximization learning are analyzed.