DocumentCode :
1909313
Title :
Lambda learning rule for feedforward neural networks
Author :
Zurada, Jacek M.
Author_Institution :
Dept. of Electr. Eng., Louisville Univ., KY, USA
fYear :
1993
fDate :
1993
Firstpage :
1808
Abstract :
Feedforward layered networks of continuous perceptrons are traditionally trained using the delta and generalized delta training rules. These learning concepts are formalized in the error backpropagation training (EBPT) concept. Although the EBPT algorithm is widely used, the lambda learning rule often offers a considerable improvement in learning. Both the rule and the complete generalized lambda learning algorithm for layered networks are outlined. Emphasis is placed on visualization of learning. Comparisons between the two learning approaches are drawn
Keywords :
backpropagation; feedforward neural nets; continuous perceptrons; error backpropagation training; feedforward neural networks; lambda learning rule; layered networks; learning concepts; Acceleration; Feedforward neural networks; Neural networks; Neurons; Visualization; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
Type :
conf
DOI :
10.1109/ICNN.1993.298831
Filename :
298831
Link To Document :
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