Title :
The inverse method for recurrent neural networks
Author :
Hatano, Shoji ; Sato, Yuji ; Hatano, Hisaaki ; Furuya, Tatsumi
Author_Institution :
Real World Comput. Partnership, Ibaraki, Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
Investigates the inverse method for recurrent neural networks. The inverse method calculates an input of a network that locally minimizes the least-mean-square error between a given output and an output from the network. The authors compare this method with: differential algorithm, BP, bounded BP, valley searching, and bounded valley searching. The authors´ experiments show that the inverse method gets better desired inputs by using bounded algorithms than by the other
Keywords :
backpropagation; function approximation; inverse problems; least mean squares methods; recurrent neural nets; search problems; bounded valley searching; differential algorithm; inverse method; least-mean-square error; recurrent neural networks; Computer networks; Equations; Error correction; Inverse problems; Kinematics; Multi-layer neural network; Neural networks; Neurons; Recurrent neural networks; Robots;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374732
