Analysis and synthesis of neural networks with lower block triangular interconnecting structure

2002 ◽  
Author(s):  
A.N. Michel ◽  
D.L. Gray
Keyword(s):  
Neural Networks ◽  
Lower Block ◽  
Analysis And Synthesis

Computational Intelligence: Retrospection and Future

2017 ◽  
Vol 21 (1) ◽  
pp. 9-12
Author(s):  
Witold Pedrycz ◽  
Keyword(s):  
Neural Networks ◽  
Fuzzy Sets ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Applied Research ◽  
Evolutionary Optimization ◽  
Retrospective View ◽  
Analysis And Synthesis

This study is aimed at a brief, carefully focused retrospective view at the Computational Intelligence – a paradigm supporting the analysis and synthesis of intelligent systems. We stress the reason behind the emergence of this discipline and identify its main features. We highlight the synergistic aspects of Computational Intelligence arising from an interaction and collaboration of fuzzy sets, neural networks, and evolutionary optimization. Some promising directions of future fundamental and applied research are also identified.

scholarly journals Towards the Rapid Design of Engineered Systems Through Deep Neural Networks

2018 ◽  
Author(s):  
Christopher McComb
Keyword(s):  
Fluid Dynamics ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Ocean Waves ◽  
Offshore Structures ◽  
Computational Approaches ◽  
Oil Platforms ◽  
Analysis And Synthesis ◽  
Engineered Systems ◽  
Computationally Intensive

The design of a system commits a significant portion of the final cost of that system. Many computational approaches have been developed to assist designers in the analysis (e.g., computational fluid dynamics) and synthesis (e.g., topology optimization) of engineered systems. However, many of these approaches are computationally intensive, taking significant time to complete an analysis and even longer to iteratively synthesize a solution. The current work proposes a methodology for rapidly evaluating and syn- thesizing engineered systems through the use of deep neural networks. The proposed methodology is applied to the analysis and synthesis of offshore structures such as oil platforms. These structures are constructed in a ma- rine environment and are typically designed to achieve specific dynamics in response to a known spectrum of ocean waves. Results show that deep learning can be used to accurately and rapidly synthesize and analyze off- shore structure.

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