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 s...

Full description

Saved in:
Bibliographic Details
Main Authors: Hiroshi Ninomiya, Shan Wan, Kabir, Humayun, Xin Zhang, Zhang, Q.J.
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Artificial neural networks
Global optimization
Microstrip filters
Microwave filters
Microwave theory and techniques
neural networks
particle swarm optimization
Solid modeling
Testing
Training
training algorithm
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0149-645X
2576-7216
DOI:10.1109/MWSYM.2008.4633002