Robust training of microwave neural network models using combined global/local optimization techniques

We present a new technique for training microwave neural network models. The proposed technique combines quasi-Newton algorithm with a recent 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 waveguide and microstrip examples are presented, demonstrating that the proposed algorithm achieves more accurate models than the conventional gradient based technique and the conventional PSO.