Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Nonlinear system modeling by competitive learning and adaptivefuzzy inference system
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using the self-organizing map
IEEE Transactions on Neural Networks
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Global modelling is a common approach to the problem of learning nonlinear dynamical input-output mappings. It consists in training a single multilayer neural network model using the whole dataset. On the other side of the spectrum stands the local modelling approach, in which the input space is divided into very small partitions and simpler (e.g. linear) models are trained, one per partition. In this paper, we propose a novel approach, called Regional Models (RM), that stands in between the global and local modelling ones. By following the approach by Vesanto and Alhoniemi [11], we first partition the input-output space using the Self-Organizing map (SOM), and then perform clustering over the prototypes of the trained SOM in order to find clusters of prototypes. Finally, a regional model is built for each cluster using the data vectors mapped to that cluster. The proposed approach is evaluated on two benchmarking problems and its performance is compared to those achieved by standard global and local models.