Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule

2001 ◽  
Vol 12 (2) ◽  
pp. 439-443 ◽  
Author(s):  
Donq Liang Lee
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Learning Rule ◽  
Complex Valued

PSEUDO-RELAXATION LEARNING ALGORITHM FOR COMPLEX-VALUED ASSOCIATIVE MEMORY

2008 ◽  
Vol 18 (02) ◽  
pp. 147-156 ◽  
Author(s):  
MASAKI KOBAYASHI
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Gradient Descent ◽  
Storage Capacity ◽  
Learning Algorithm ◽  
Inverse Matrix ◽  
Associative Memories ◽  
Hebb Rule ◽  
Complex Valued ◽  
Pseudo Inverse

HAM (Hopfield Associative Memory) and BAM (Bidirectinal Associative Memory) are representative associative memories by neural networks. The storage capacity by the Hebb rule, which is often used, is extremely low. In order to improve it, some learning methods, for example, pseudo-inverse matrix learning and gradient descent learning, have been introduced. Oh introduced pseudo-relaxation learning algorithm to HAM and BAM. In order to accelerate it, Hattori proposed quick learning. Noest proposed CAM (Complex-valued Associative Memory), which is complex-valued HAM. The storage capacity of CAM by the Hebb rule is also extremely low. Pseudo-inverse matrix learning and gradient descent learning have already been generalized to CAM. In this paper, we apply pseudo-relaxation learning algorithm to CAM in order to improve the capacity.

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