open access

https://hdl.handle.net/2115/64600

Composite Artificial Neural Network : Selection-type Neural Network Ensembles in Dynamic System Control by Unsupervised Learning

日本語

particle swarm optimization

2013

公益社団法人 精密工学会

精密工学会誌

79(6)

552-558

This paper describes a novel method of combining artificial neural networks (ANNs), the composite artificial neural network (CANN), to improve performance of evolving ANNs (EANNs) in dynamic system control problems. Methods of combining ANNs by majority voting or averaging are not effective in controlling dynamic systems by EANNs. Unsupervised learning of EANNs is mathematically described, and then it is shown that the reason for ineffectiveness depends on the mechanism of evaluating ANNs indirectly by states. To avoid this problem, the CANN selects the suitable ANN by a high-level ANN to combine some ANNs. In numerical experiments, a flapping flight model is controlled by a common EANN and the CANN. The model motion is calculated by physical engine PhysX, and a common EANN and the CANN are optimized by the particle swarm optimization (PSO) respectively. The experiments show that the average evaluation of the CANN is 6.46% higher than that of a common EANN for the same computational time.

学術雑誌論文

Version of Record

0912-0289

1882-675X

AN1003250X