Title :
Backpropagation algorithm in higher order neural network
Author :
Chang, Chirho ; Cheung, J.Y.
Author_Institution :
Oklahoma Univ., Norman, OK, USA
Abstract :
The supervised backpropagation learning scheme is used to develop a training algorithm for multilayer higher-order neural networks (HONNs). By restructuring the basic HONN architecture, the traditional backpropagation algorithm can be extended to multilayer HONNs. The TC pattern recognition problem is used to compare the performances of various HONNs with different numbers of hidden layers, different numbers of processing elements, and different orders. Simulation results show that, in many causes, the HONN with the same number of training iterations worked better than the conventional first-order networks
Keywords :
backpropagation; neural nets; pattern recognition; multilayer higher-order neural networks; pattern recognition; supervised backpropagation learning scheme; training iterations; Artificial neural networks; Backpropagation algorithms; Control system synthesis; Image analysis; Image recognition; Intelligent networks; Multi-layer neural network; Neural networks; Pattern recognition; Power system modeling;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.227123
