MODULAR NEURAL NETWORKS: A SURVEY

1999 ◽  
Vol 09 (02) ◽  
pp. 129-151 ◽  
Author(s):  
GASSER AUDA ◽  
MOHAMED KAMEL
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Decomposition Techniques ◽  
Task Decomposition ◽  
Modular Neural Networks ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Learning Schemes

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.

Artificial Neural Networks for Decision-Making in Urologic Oncology

2003 ◽  
Vol 43 (6) ◽  
pp. 596-603 ◽  
Author(s):  
Theodore Anagnostou ◽  
Mesut Remzi ◽  
Michael Lykourinas ◽  
Bob Djavan
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Urologic Oncology ◽  
Artificial Neural

Learning in Artificial Neural Networks: A Statistical Perspective

1989 ◽  
Vol 1 (4) ◽  
pp. 425-464 ◽  
Author(s):  
Halbert White
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Theory ◽  
Comprehensive Treatment ◽  
Training Methods ◽  
Network Learning ◽  
Advantages And Disadvantages ◽  
Systems Identification ◽  
Artificial Neural ◽  
Insight Into

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.

scholarly journals Modelling and simulation of artificial neural networks using software SIMBRAIN

2021 ◽  
Vol 7 ◽  
pp. 71-81
Author(s):  
Yoana Ivanova
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Morphological Analysis ◽  
Critical Infrastructure ◽  
Modelling And Simulation ◽  
Advanced Technology ◽  
Artificial Neural ◽  
And Robotics

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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