A local neuro-fuzzy network for high-dimensional models and optimization

In this paper a new iterative construction algorithm for local model networks is presented. The algorithm is focussed on building models with sparsely distributed data as they occur in engine optimization processes.The validity function of each local model is fitted to the available data using statistical criteria along with regularization and thus allowing an arbitrary orientation and extent in the input space. Local models are consecutively placed into those regions of the input space where the model error is still large thus guaranteeing maximal improvement through each new local model. The orientation and extent of each validity function are also adapted to the available training data such that the determination of the local regression parameters is a well-posed problem. The regularization of the model can be controlled in a distinct manner using only two user-defined parameters. In order to assess the quality of the obtained model, the algorithm also provides accurate model statistics. Different examples illustrate the efficiency of the proposed algorithm.One illustrative example shows how local models are adapted to the shape of the target function in the presence of noise. A second example shows results obtained with measurement databases of IC-engines.