Tensorial Biometric Signal Recognition Based on Feed Forward Neural Networks with Random Weights

2015 ◽  
pp. 609-620
Author(s):  
Fan Zhang ◽  
An Qin
Keyword(s):  
Neural Networks ◽  
Signal Recognition ◽  
Feed Forward ◽  
Random Weights ◽  
Feed Forward Neural Networks

Extended feed forward neural networks with random weights for face recognition

2014 ◽  
Vol 136 ◽  
pp. 96-102 ◽  
Author(s):  
Jing Lu ◽  
Jianwei Zhao ◽  
Feilong Cao
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Feed Forward ◽  
Random Weights ◽  
Feed Forward Neural Networks

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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