A New Model of BAM: Alpha-Beta Bidirectional Associative Memories

2007 ◽  
Vol 2 (4) ◽  
Author(s):  
María Elena Acevedo Mosqueda ◽  
Cornelio Yáñez Márquez ◽  
Itzamá López Yáñez
Keyword(s):  
Associative Memories ◽  
New Model ◽  
Alpha Beta

A NEW MODEL OF NEURAL ASSOCIATIVE MEMORIES

1994 ◽  
Vol 05 (01) ◽  
pp. 39-47 ◽  
Author(s):  
JIANBIN HAO ◽  
JOOS VANDEWALLE
Keyword(s):  
Fixed Point ◽  
Hamming Distance ◽  
Layer Structure ◽  
Design Rule ◽  
Associative Memories ◽  
New Model ◽  
Attraction Basin ◽  
Model Features ◽  
The Given ◽  
Discrete Hopfield Model

In this paper, we present a new model of discrete neural associative memories and its design rule. The most important feature of this new model is that a static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The new model features a two-layer structure, with feedforward connections only and uses two kinds of neurons which implement different output functions. Another important feature is that this new model employs an extremely simple weight setup rule and all the resulted weights can only assume two different values, −1 and +1, which facilitates the VLSI implementation. Compared to the famous discrete Hopfield model designed with the well-known Hebbian rule or any other rule, the new model can guarantee all the given patterns to be stored as fixed points. Moreover, each fixed point is surrounded by an attraction basin (which is a ball in the Hamming distance sense) with the maximal possible radius. The performances of the new model are compared through some illustrative examples with those of the Hopfield associative memory designed using different methods.

scholarly journals New Model of Heteroasociative min Memory Robust to Acquisition Noise

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 148
Author(s):  
Julio César Salgado-Ramírez ◽  
Jean Marie Vianney Kinani ◽  
Eduardo Antonio Cendejas-Castro ◽  
Alberto Jorge Rosales-Silva ◽  
Eduardo Ramos-Díaz ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Sufficient Conditions ◽  
Distance Transform ◽  
Associative Memories ◽  
Gray Scale ◽  
New Model ◽  
Knowing How ◽  
Proposed Model ◽  
Max Algebra ◽  
Mixed Noise

Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

A New Model of BAM: Alpha-Beta Bidirectional Associative Memories

2006 ◽  
pp. 286-295 ◽  
Author(s):  
María Elena Acevedo-Mosqueda ◽  
Cornelio Yáñez-Márquez ◽  
Itzamá López-Yáñez
Keyword(s):  
Associative Memories ◽  
New Model ◽  
Alpha Beta

The Hitachi Model HS-8 Electron Microscope

1967 ◽  
Vol 25 ◽  
pp. 238-239
Author(s):  
H. Akabori ◽  
K. Nishiwaki ◽  
K. Yoneta
Keyword(s):  
Electron Microscope ◽  
New Model ◽  
Predecessor Model

By improving the predecessor Model HS- 7 electron microscope for the purpose of easier operation, we have recently completed new Model HS-8 electron microscope featuring higher performance and ease of operation.

516: A Simplified Method to Estimate the Absolute Renal Uptake of 99MTC-DMSA: Evaluation with a New Model using the Radioactivity of Nephrectomy Specimens as Reference

2005 ◽  
Vol 173 (4S) ◽  
pp. 140-141
Author(s):  
Mariana Lima ◽  
Celso D. Ramos ◽  
Sérgio Q. Brunetto ◽  
Marcelo Lopes de Lima ◽  
Carla R.M. Sansana ◽  
...  
Keyword(s):  
New Model ◽  
Renal Uptake ◽  
Simplified Method ◽  
The Absolute

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

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