Kadlec, P. and Gabrys, B., 2008. Learnt Topology Gating Artificial Neural Networks. In: Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on. IEEE, pp. 2604-2611.
Full text available as:
This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed Learnt Topology Gating Artificial Neural Networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are Artificial Neural Networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method.
|Item Type:||Book Section|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||Faculty of Science and Technology|
|Deposited By:||INVALID USER|
|Deposited On:||19 Dec 2008 18:16|
|Last Modified:||10 Sep 2014 14:43|
Downloads per month over past year
|Repository Staff Only -|