Title :
Hierarchical artificial neural network architecture
Author :
Speer, R.K. ; Moore, W.E.
Author_Institution :
Sch. of Inf. Technol., Charles Sturt Univ., Bathurst, Qld., Australia
Abstract :
This paper presents a hierarchical artificial neural network (HANN) architecture which is shown to be superior to the traditional three layer feedforward neural network for the neurocontrol of mobile robots in terms of robustness and adaptability with implications for application areas other than robotics
Keywords :
backpropagation; feedforward neural nets; mobile robots; neural net architecture; neurocontrollers; stability; adaptability; backpropagation; hierarchical neural network; mobile robots; neurocontrol; robustness; Artificial neural networks; Control systems; Information technology; Intelligent control; Intelligent robots; Mobile robots; Neurons; Robot control; Robustness; Testing;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687192
