An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer....

Christopher Lieu, MD William J. Janssen, MD Sanjay Saint, MD, MPH Gurpreet Dhaliwal, MD 1 Department of Medicine, University of Colorado Health Sciences Center, Denver, Colorado. 2 Department of Medicine, National Jewish Medical and Research Center, Denver, Colorado. 3 Ann Arbor Veterans Affairs Health Services Research and Development Center of Excellence, Ann Arbor, Michigan. 4 Patient Safety...

In this work an initial approach to design Artificial Neural Networks (ANN) using Genetic Algorithms (GA) is tackle. A key issue for these kind of approaches is what information contains, or is included, in the chromosome that represent an ANN, and there are two principal ideas about these question: first, information about parameters of the topology, architecture, learning parameters, etc. of ...

Epidemiology and Biostatistics, Hunter College, City University of New York, CUNY School of Public Health, New York, NY, USA, CUNY Institute for Demographic Research, New York, NY, USA, Inter-University Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA, Departm...

This study presents the effects of project uncertainties on nonlinear time-cost tradeoff (TCT) profile of real life engineering projects by the fusion of fuzzy logic and artificial neural network (ANN) models with hybrid meta-heuristic (HMH) technique, abridged as Fuzzy-ANN-HMH. Nonlinear time-cost relationship of project activities is dealt with ANN models. ANN models are then integrated with ...

Ecosystem of arid and semiarid regions of the world, much of the country lies in the sensitive and fragile environment Canvases are that factors in the extinction and destruction are easily destroyed in this paper, artificial neural networks (ANNs) are introduced to obtain improved regional low-flow estimates at ungauged sites. A multilayer perceptron (MLP) network is used to identify the funct...

Definition of Artificial Neural Networks (ANNs) is made by computer scientists, artificial intelligence experts and mathematicians in various dimensions. Many of the definitions explain ANN by referring to graphics instead of giving well explained mathematical definitions; therefore, misleading weighted graphs (as in minimum cost flow problem networks) fit the definition of ANN. This study aims...

The Artificial Neural Network (ANN) is a powerful data-driven model that can capture and represent both linear and non-linear relationships between input and output data. Hence, ANNs have been widely used for the prediction and forecasting of water quality variables, to treat the uncertainty of contaminant source, and nonlinearity of water quality data. However, the initial weight parameter pro...

The prediction of solar radiation is important for several applications in renewable energy research. Solar radiation is predicted by a number of solar radiation models both conventional and Artificial Neural Network (ANN) based models. There are a number of meteorological and geographical variables which affect solar radiation prediction, so identification of suitable variables for accurate so...

Artiicial Neural Networks(ANN) play an important role in developing robust Knowledge Based Systems(KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specii...