A constructive learning algorithm for local model networks
Roderick Murray‐Smith, H. Gollee · 1994
Local Model Networks are flexible architectures for the representation of complex non-linear dynamic systems. The local nature of the representation leads to a modular network which can integrate a variety of paradigms (neural nets, statistics, fuzzy systems and a priori mathematical models), but because of the power of the local models, the architecture is less sensitive to the curse of dimensionality than other local representations, such as Radial Basis Function networks. The concept of `locality' is a difficult one to define, and tends to vary over a problem's input space, so a constructive structure identification algorithm is presented which automatically defines a suitable model structure on the basis of the observed data from the process being identified. Local learning algorithms are introduced for the local model parameter optimisation, which save computational effort and produce more interpretable and robust models. 1. Introduction Computationally intensive learning systems...