Parameter identification from hybrid model using PSO and penalty functions

This work studies the parameter identification of a hybrid model with Particle Swarm Optimization. A hybrid model is based on selection functions that allow the switching between simple mathematical expressions in order to describe a complex behavior. In this work two performance functions are proposed to perform the identification: The former considers a switching between functions on their structure. The latter implements function penalty functions in order to avoid the evaluation of the selection functions. These functions test for the parameter identification of a Shape Memory Alloy model under a numerical simulation. The quality of the computed estimates is tested using statistical tools to assure repeatability and to verify the influence of the performance functions.