Using artificial neural networks to improve decision making in apparel supply chain systems

Author(s):  
P.C.L. Hui ◽  
T.-M. Choi
2003 ◽  
Vol 43 (6) ◽  
pp. 596-603 ◽  
Author(s):  
Theodore Anagnostou ◽  
Mesut Remzi ◽  
Michael Lykourinas ◽  
Bob Djavan

1999 ◽  
Vol 09 (02) ◽  
pp. 129-151 ◽  
Author(s):  
GASSER AUDA ◽  
MOHAMED KAMEL

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.


2021 ◽  
Vol 7 ◽  
pp. 71-81
Author(s):  
Yoana Ivanova

This paper is considered to be a continuation of a previous publication devoted to tendencies in the applications of advanced technology solutions to strengthen the cybersecurity of critical infrastructure (Yearbook Telecommunications, vol. 6, 2019). The specificity of the research is related to tracing the evolution of artificial neural networks (ANN) from their establishment to their modelling and simulation. The theoretical framework involves a well-supported rationale by some practical examples of advanced methods of design and simulation of ANN using SIMBRAIN. These methods are applicable in Cognitive science and Robotics because of their contribution to scientific researches related to study of perceptions and behaviors, abilities of decision making, pattern recognition and morphological analysis and etc.


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