Neural network digital hardware implementation

Abstract

This thesis presents a digital hardware implementation of an artificial neuron with learning ability using the QuartusII 5.1sp1 web edition software on Altera's University Program Development Board (UP2). The learning method implemented is neither backpropagation nor conjugate gradient, but the weight simultaneous perturbation. By combining this method with a pulse density system and using a Field Programmable Gate Array, an interesting artificial neuron hardware architecture is obtained. Finally, two applications of the neuron implementation are presented: an analog function and a digital function.