Self Growing Binary Tree Neural Network for the Identification of MulticlassSystems

摘要

A self growing binary tree neural network is introduced for the onlineidentification of multiclass systems. Instead of solving the traditional two class problem with a single neuron a time shrinking threshold logic unit is introduced such that the modified neuron has the capability of partitioning the raw data records into three different regions. Incorporating a least mean squares learning algorithm provides the capability of detecting and creating new classes and this allows clustering and partitioning of the raw data records into model classes and yields estimates of the parameters. The models which describe the behavior of the system at different operating regions can be recovered by inspection of the connection weights of the individual neurons. Optimization procedures for online estimation are proposed. Simulation studies are included to illustrate the concepts.