Generalized Memory Polynomial Model Dimension Selection Using Particle Swarm Optimization

This letter presents a new method which uses particle swarm optimization and the Akaike information criterion for determining the dimensions of nonlinear amplifier behavioral models, applied to the generalized memory polynomial model. Determining the size of this model has always been a challenge as it depends on eight parameters, and the proposed method provides a fast and efficient solution which can discover the smallest possible model at a very short amount of time.