A Train Algorithm of Multi-Aggregation Process Neural Networks Based on Chaos Genetic Optimization
Yue-Wu Pang, Shao-Hua XU
Available Online November 2012.
- https://doi.org/10.2991/citcs.2012.147How to use a DOI?
- process neural networks, train algorithm, chaos genetic algorithm, mixed optimization strategy
- Aiming at the learning problem of multi-aggregation process neural networks, an optimization train method based on chaos genetic algorithm (CGA) with lowest mean square algorithm (LMS) is proposed in the paper. The learning problem of the network weight functions is converted to the training of basis expansion coefficients through adopting the function orthogonal basis expansion method. The lowest mean square error as the train objective function, the global optimal solution of network parameters is solved in feasible solution space using the chaos rail to traverse the search of CGA. The application in multi-variant dynamic signal identification proved that the algorithm proposed in the paper improved the training efficiency and stability greatly
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yue-Wu Pang AU - Shao-Hua XU PY - 2012/11 DA - 2012/11 TI - A Train Algorithm of Multi-Aggregation Process Neural Networks Based on Chaos Genetic Optimization BT - 2012 National Conference on Information Technology and Computer Science PB - Atlantis Press SP - 571 EP - 574 SN - 1951-6851 UR - https://doi.org/10.2991/citcs.2012.147 DO - https://doi.org/10.2991/citcs.2012.147 ID - Pang2012/11 ER -