A New Neural Network Modeling Approach Based on a Correction Model Concept

Neural networks have recently gained attention as unconventional yet effective alternatives for component modeling. One of the most commonly used neural networks, namely the multilayer perceptrons (MLP) could sometimes fail to model highly nonlinear input-output behaviors accurately. Advanced neural networks (e.g. knowledge based neural networks) can be employed; however, such networks suffer from an increased complexity both in terms of their structures and training methods. In this paper, we propose a neural network modeling approach based on a novel correction model concept. This approach helps accurately model complicated behaviors using simple 3-layer MLP networks. Both active and passive examples are presented.