Flexible models with evolving structure

A flexible model in the form of an artificial neural network (NN) with evolving structure (eNN) is represented in the paper in the form of the evolving fuzzy Takagi-Sugeno model. It falls into the same category of models as the recently introduced evolving rule-based (eR) models. The learning algorithm is incremental, unsupervised and is based on the on-line identification of Takagi-Sugeno type quasilinear models. Both eR and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. Essentially, it represents a Takagi-Sugeno model with gradually evolving set of rules, determined on-line. This approach has potential in both modeling and control using indirect learning mechanisms. Its computational efficiency is based on the non-iterative and recursive procedure, which combines a Kalman filter with proper initializations, and online unsupervised clustering. eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive non-linear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, etc. are possible directions of their use in the future research.