Improvement of the backpropagation algorithm for training neural networks

Computers and Chemical Engineering

Volumn 14, Issue 3, 1990, Pages 337-341

  Leonard, J ^a   Kramer, M A ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICAL ENGINEERING--COMPUTER APPLICATIONS; COMPUTER PROGRAMMING--ALGORITHMS;

ARTIFICIAL NEURAL NETWORKS; BACKPROPAGATION ALGORITHM IMPROVEMENT; MALFUNCTION DIAGNOSIS; NEURAL NETWORKS TRAINING;

NEURAL NETWORKS;

EID: 0025403609     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/0098-1354(90)87070-6     Document Type: Article

References (13)

1. Improving the convergence of back-propagation learning with second order methods
2. Faster-learning variations on back-propagation: an empirical study
3. Function minimization by conjugate gradients
4. Artificial neural network models of knowledge representation in chemical engineering
5. Improving the learning rate of back-propagation with the gradient reuse algorithm
6. An adaptive least squares algorithm for the efficient training of artificial neural networks
7. Optimal algorithms for adaptive networks: second order back propagation, second order direct propagation, and second order Hebbian learning
8. Learning internal representations by error propagation
9. Fast learning in artificial neural systems: multilayer perceptron training using optimal estimation
10. An improved three-layer backpropagation algorithm
11. Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimization
12. Beyond regression: new tools for prediction and analysis in the behavioral sciences