Title :
Self-organizing network for optimum supervised learning
Author :
Tenorio, Manoel F. ; Lee, Wei-Tsih
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
fDate :
3/1/1990 12:00:00 AM
Abstract :
A new algorithm called the self-organizing neural network (SONN) is introduced. Its use is demonstrated in a system identification task. The algorithm constructs a network, chooses the node functions, and adjusts the weights. It is compared to the backpropagation algorithm in the identification of the chaotic time series. The results show that SONN constructs a simpler, more accurate model, requiring less training data and fewer epochs. The algorithm can also be applied as a classifier
Keywords :
identification; learning systems; neural nets; artificial intelligence; epochs; learning systems; node functions; self-organizing neural network; supervised learning; system identification; training data; Chaos; History; Neural networks; Organizing; Parameter estimation; Self-organizing networks; Signal processing; Supervised learning; System identification; Training data;
Journal_Title :
Neural Networks, IEEE Transactions on
