Selection and Combination Strategy of ANN Systems
Pp. 154-170 (17)
It is often required to select and combine two or more neural networks in
order to process a given data. The aim and objectives of this chapter is to describe the
selection and combination strategy of ANN systems. Two methods of ANNs’ selection
and combination that are derived from principle are described in detail. Manual
selection and combination which is possible only if it involve few networks are not
considered. Also not considered are heuristically determined set of networks, because
of additional large experimentation that must be performed to select a suitable number
and configuration of ANNs. These hindrances are relieved by the selection and
combination strategy described in this chapter. The chapter has described two methods
of selection and combination of ANNs which may be applied to minimize ANN’s
network errors. The selection and combination strategies descried in this chapter are
principled, more robust, and of wider applicability than other alternatives.
Classifier selection, Combiner configuration, Combiner engine,
Combiner unit, Converter, Error – independent, Factorial selection, Fusion, Fuzzy –
neuron, Group method, Interpreter, Kolmogorov-Gabor Polynomial, Main-group,
Minimum complexity, Pool of networks, Pre-group, Statistical selection,
Topology, Volterra series.
University of Kent, United Kingdom.