Abstract
This paper proposes a composite artificial neural network (CANN). The CANN is a method that contains concepts of an evolutionary artificial neural network, a neural network ensemble and subsumption architecture, and designed for efficient robot control. In the CANN, while low-level ANNs work as actual controllers for calculating outputs, a high-level work as a selector. The high-level ANN works up some optimized ANNs, which output real values, into a controller. In order to verify performance of the CANN, numerical experiments are carried out. An artificial flying creature (AFC) is controlled by the CANN for flying to a target point. Motions of the AFC is calculated by a virtual physics environment, which consists of functions of a physical engine PhysX and a simple drag force calculation. Experimental results show that performance of the CANN is higher than that of a simple ANN.
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© 2013 Springer-Verlag Berlin Heidelberg
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Ooe, R., Suzuki, I., Yamamoto, M., Furukawa, M. (2013). Composite Artificial Neural Network for Controlling Artificial Flying Creature. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_82
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DOI: https://doi.org/10.1007/978-3-642-33932-5_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33931-8
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