Assembling engineering knowledge in a modular multi-layer perceptron neural network

The popular multi-layer perceptron (MLP) topology with an error-back propagation learning rule doesn't allow the developer to use the (explicit) engineering knowledge as available in real-life problems. Design procedures described in literature start either with a random initialization or with a 'smart' initialization of the weight values based on statistical properties of the training data. This article presents a design methodology that enables the insertion of pre-trained parts in a MLP network topology and illustrates the advantages of such a modular approach. Furthermore we will discuss the differences between the modular approach and a hybrid approach, where explicit knowledge is captured by mathematical models. In a hybrid design a mathematical model is embedded in the modular neural network as an optimization of one of the pre-trained subnetworks or because the designer wants to obtain a certain degree of transparency of captured know-ledge in the modular design.