Artificial Neural Networks and Genetic Algorithms for the Modeling, Simulation, and Performance Prediction of Solar Energy Systems

2013 ◽  
pp. 225-245 ◽  
Author(s):  
Soteris A. Kalogirou
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Performance Prediction ◽  
Energy Systems ◽  
Modeling Simulation ◽  
Artificial Neural ◽  
And Performance

scholarly journals An Evolutionary Perspective of the Relationship Between Corporate Strategy and Performance, Through the Use of Artificial Neural Networks and Genetic AlgorithmsHttp://Dx.Doi.Org/10.5585/Riae.V9i3.1689

2011 ◽  
Vol 9 (3) ◽  
pp. 63-87
Author(s):  
Alexandre Teixeira Dias ◽  
Carlos Alberto Gonçalves ◽  
Gustavo Ferreira Mendes de Souza
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Corporate Strategy ◽  
Evolutionary Perspective ◽  
Corporate Strategies ◽  
Artificial Neural ◽  
Improved Performance ◽  
And Performance ◽  
The Relationship

This study aims to contribute to the understanding of the relationship between Corporate Strategy and Performance, from the perspective of the Evolutionary Theory. As methods of data processing, obtained in secondary databases, we used artificial neural networks and genetic algorithms. The results of processing neural networks and genetic algorithms demonstrate the importance of corporate strategies in determining performance. The evolutionary perspective emphasizes the importance of investing in operations as a factor influencing the adequacy of the organization, in order to achieve an improved performance, in addition to establishing relationships with other organizations, through members of the board.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

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