Artificial Neural Networks’ Modeling
Modelling and Temperature Control of Heat Exchanger process
... design. Artificial neural networks (ANN) are effective in modeling of non linear multi variables so modeling of heat exchanger process is accomplished using optimized architecture of ...
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The Precipitation Modeling through the CPSO-based Artificial Neural Networks
... the neural networks by the ...of modeling such social behavior is a search process wherein the particles are directed towards some better spots in terms of ...
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Wheat Yield Prediction Using Artificial Neural Network and Crop Prediction Techniques (A Survey)
... Artificial neural networks, which are nonlinear data-driven approaches as opposed to the above model-based nonlinear methods, are capable of performing nonlinear modeling without a priori ...
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Study and Overview on System Feedback Representations in Control Modeling with Artificial Neural Networks (ANN) Platform
... of artificial intelligence is the development of standard models or appropriate algorithms that require machines to perform experimental tasks, at which humans are currently exceeding ...A neural network is ...
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On Comparative Study for Two Diversified Educational Methodologies Associated with “How to Teach Children Reading Arabic Language?” (Neural Networks’ Approach)
... of artificial neural networks (ANNs) after its applications at the interdisciplinary discipline incorporating neu- roscience, education, and cognitive ...
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A Theoretical Framework for Property Enhancement of Smart Materials Based on Manufacturing Process Modeling Using Artificial Neural Networks.
... the artificial neural network has to figure out the inherent structure based on data which increases the amount of data and computational cost ...hierarchical modeling of manufacturing process chains ...
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Using Artificial Neural Networks in Stochastic Differential Equations Based Software Reliability Growth Modeling
... Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers ...
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Artificial Neural Networks for Event Based Rainfall Runoff Modeling
... using neural networks. Hjelm- felt and Wang [7] developed a neural network based on the unit hydrograph theory for the Goodwater Creek wa- tershed in central ...two neural networks, Zhu ...
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Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement Using Feed-Forward and Generalized Regression Neural Networks
... Similar to the human brain, a FFNN utilizes numerous basic computational components, named artificial neurons, connected by variant weights [18]. Components of an artificial neuron is demonstrated in Figure ...
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Frequency domain fatigue analysis of dynamically sensitive structures
... and Artificial Neural Network (ANN) have been applied to study the effect of ...developed Artificial Neural Network systems, back- p rop agation , a method of training a neural network ...
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Study of Membrane Transport for Protein Filtration Using Artificial Neural Networks
... In an another study, which included extraction of proteins from colloidal suspension, Albert S. Kim and Huaiqun Chen(University of Hawaii)used an Artificial Neural Network as the alternative approach to ...
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Modelling Metal cutting Parameters Using Intelligent Techniques
... the modeling of metal ...on artificial intelligence are often used for this ...the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature ...
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Modeling flexibility using artificial neural networks
... The flexibility of a particular DER or an aggregate of multiple DERs can be described as the set of all feasible load profiles for a given time frame. Feasible in the context of DER load profiles refers to load profiles ...
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Research on Classification of E-shopper Based on Neural Networks and Genetic Algorithm
... and neural network etc are usually used to mine e-shopper’s transaction database in order to classify ...And artificial neural network is the important one of the ...fact, neural ...
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Artificial Neural Networks A Review of Applications of Neural Networks in the Modeling of HIV Epidemic
... The authors concluded that the MLP network trained using backpropagation algorithm produced the best performance with 89.80% accuracy as compared to Levenberg-Marquardt and Bayesian rule[r] ...
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Modeling and Optimization of Roll-bonding Parameters for Bond Strength of Ti/Cu/Ti Clad Composites by Artificial Neural Networks and Genetic Algorithm
... probabilistic neural network [PNN] and generalized regression neural network [GRNN]), multilayer perceptron (MLP) neural networks are quite common and applied for the present ...MLP ...
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Analysis of cardiovascular (cvd)/coronary heart diseases(chd) using artificial neural network (ann)
... ANN. Artificial Neural Network (ANN) has extensive application to biomedical ...systems. Neural networks learn by example, so the details of how to identify diseases are not ...
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Estimation of groundwater level using a hybrid genetic algorithm-neural network
... BP neural network consists of five input variables, seven hidden neurons with hyperbolic tangent function and one output variable with a linear activation function, transform the sum of all the weighted inputs ...
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A. Artificial Neural Networks
... Recently, artificial neural networks have been used as an auxiliary tool to predict stock price time series ...of neural networks lie in their ability to model nonlinear relations ...
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Supervised learning methods in modeling of CD4+ T cell heterogeneity
... for modeling enteric immunity ...organism. Modeling a complex system at four levels of magnitude – multiscale modeling (MSM) – poses new set of ...Multiscale modeling requires considering ...
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