Internal feedback neuron networks for modeling of an industrial furnace

Most of the physical processes are dynamical in nature. Modeling of such systems using process physics is a complicated task involving a lot of time and effort. Artificial neural networks with their self learning capabilities offer promise for an alternative way of modeling. This paper presents a dynamic neural network architecture which has the potential for use in dynamic system identification. In this network, the behaviour of each neuron is made dynamic by incorporating feedback connections in it. The delayed outputs of each neuron are fed back to itself as additional inputs through weights. This particular network architecture is termed as internal feedback neuron network. The network is trained using a specially derived gradient based training algorithm. Simulations have been carried out on an industrial furnace and satisfactory results have been obtained