Design of neural networks for multi-value regression
K.K. Lee, Tong Lee · 2002
The problem of multi-value regression estimation with neural network architecture is addressed. We only consider feedforward neural networks (FNN) and we also confine the multi-value regression problems to those mapping N input variables to a single output, while the numbers of output values may be different for different input. We propose a modular neural network approach to solve this problem with each module handling a sub-range of the original one such that each module now only handles a many-to-one or one-to-one regression estimation. With such an approach, a verification process is necessary to determine which module provides the correct output value and two implementations are discussed. Several examples are used to illustrate the proposed method.