A self-organized neural network based on the multi-model partitioning theory
This work faces the problem of automatically exploring the optimal size of a neural network, required to solve a class of specific problems. We propose a general self-organized neural network based on adaptive signal processing techniques. Specifically, the solution is based on the combination of the multi-model partitioning theory and a bank of neural units that use the localized extended Kalman filter as training algorithm. Thus the problem is reduced to selecting the correct model, among a set of `candidate models. The output of such a system is proved to satisfy the universal approximation theorem. Three different implementations are presented that perform better than other existing techniques. The new algorithm is data driven and the resulting networks are recurrent and adaptive, in the sense that they are able to track successfully the changes in the model structure, in real time. Furthermore, the network can be implemented in a parallel environment and a VLSI implementation is feasible.
multi-model partitioning theory optimal size universal approximation theorem adaptive neural network