Extreme Learning Machine with Learnable Activation Functions for Machine Control

Searching for activation functions has the potential to tackle generalization across different problem domains. In this paper, we study the Extreme Learning Machine with Learnable Activation Functions (ELMA), leveraging ordered trees and bilevel optimization via Differential Evolution algorithms. We analyze the effectiveness in learning function surrogates for the inverse inference of gains and parameters in high-performance machine control. Our computational experiments, spanning diverse machine control scenarios including motor position control, motor velocity control, crane stabilization, inverted pendulum, and magnetic levitation demonstrate that ELMA achieves superior learning and generalization performance compared to other existing frameworks.