Implementation of quadric perceptron with hardlims activation function in a FPGA for nonlinear pattern classification

This paper deals with the design and implementation of an artificial neural network for pattern classification in the FPGA EP2C20F484C7. A perceptron with quadratic decision boundary is used as nonlinear classification system, but using a hardlims as activation function, instead of a sigmoid function. The training algorithm is similar to the used in conventional perceptron. The elimination of the sigmoid function makes simpler the implementation of quadratic perceptrons. As the mentioned FPGA does not do neither float-point nor fixed-point multiplications, the synaptic weights were normalized to integers. The proposed quadratic perceptron is tested in a set of classification problems and compared with multilayer perceptron. Example of experimental implementation of the proposed classification system is shown, including parameters about computational cost.