A hybrid global/local optimization technique for robust training of microwave neural network models

Abstract

This paper describes a new technique for training microwave neural network models. The proposed technique combines quasi-Newton algorithm with a global optimization algorithm called particle swarm optimization (PSO). The quasi-Newton process for searching optimal solutions is incorporated into PSO to speed up local search, while the PSO performs global search avoid being trapped in local minima of training. The overall algorithm iterates between quasi-Newton and PSO. Neural network training for microwave circuit modeling, such as waveguide and microstrip examples is presented, demonstrating that the proposed algorithm achieves more accurate models than the conventional gradient based technique and the conventional PSOs.