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This paper documents the optimization of architecture in accordance with Bejan's Constructal theory.
The optimization of architecture and weights of ANNs is based on Particle Swarm Optimization with early stopping criteria.
The present study aimed to investigate the use of optimization of architecture and weights of Artificial Neural Network (ANN) for identification of the factors related to CMDs.
Following the optimization of architecture and configuration of ANNs, the training data are used for training the networks to predict the value of major principal stress at failure from the values of minor and intermediate principal stresses.
The integrative method illustrated in this paper is generally applicable to the optimization of architecture in other systems where all the functions are driven by the exergy of the fuel installed onboard.
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This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights.
The efficiency of GA in design and optimization of the architecture of ANN was investigated.
Thus optimization of cathode architecture by adding of large-scale cavities enables cell capacity enhancement.
Extensive optimization of network architecture, connectivity and pre-processing is presented.
The successful operation of such a device requires, in addition to the optimization of its architecture, the use of high quality thin films with smooth surface morphology.
It is becoming increasingly evident that optimization of root architecture for resource capture is vital for enabling the next green revolution.
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