An artificial neural net attraction model (ANNAM) to analyze market share effects of marketing instruments

2001 ◽  
Vol 53 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Harald Hruschka
Keyword(s):  
Market Share ◽  
Neural Net ◽  
Artificial Neural Net ◽  
Attraction Model ◽  
Artificial Neural

Genetic Evolution for the Development of Robust Artificial Neural Network Logic Gates

2009 ◽  
Author(s):  
Anthony M. Roy ◽  
Erik K. Antonsson ◽  
Andrew A. Shapiro
Keyword(s):  
Logic Gate ◽  
Logic Gates ◽  
Design Methods ◽  
Neural Net ◽  
C Programs ◽  
Artificial Neural Net ◽  
Classical Design ◽  
Artificial Neural ◽  
Exclusive Or ◽  
Or Logic Gate

Control tasks involving dramatic non-linearities, such as decision making, can be challenging for classical design methods. However, autonomous stochastic design methods have proved effective. In particular, Genetic Algorithms (GA) that create phenotypes by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as artificial neural net design. This paper outlines an evolutionary algorithm that creates C++ programs which in turn create Artificial Neural Networks (ANNs) that can functionally perform as an exclusive-OR logic gate. Furthermore, the GAs are able to create scalable ANNs robust enough to feature redundancies that allow the network to function despite internal failures.

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images

2020 ◽  
Vol 39 (2) ◽  
pp. 366-376 ◽  
Author(s):  
Ivan S. Klyuzhin ◽  
Ju-Chieh Cheng ◽  
Connor Bevington ◽  
Vesna Sossi
Keyword(s):  
Neural Net ◽  
Dynamic Pet ◽  
Artificial Neural Net ◽  
Artificial Neural

scholarly journals Artificial neural net based noise cancellor

2005 ◽  
Author(s):  
P.K. Dash ◽  
S. Saha ◽  
P.K. Nanda ◽  
H.P. Khincha
Keyword(s):  
Neural Net ◽  
Artificial Neural Net ◽  
Artificial Neural
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