Artificial Neural Networks_New Research-Nova Science(2017).pdf

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This current book provides new research on artificial neural networks (ANNs). Topics discussed include the application of ANNs in chemistry and chemical engineering fields; the application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents; the use of ANNs for solar radiation estimation; the use of in silico methods to design and evaluate skin UV filters; a practical model based on the multilayer perceptron neural network (MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut and exit cut, of a milling tool; parameter extraction of small-signal and noise models of microwave transistors based on ANNs; and the application of ANNs to deep-learning and predictive analysis in semantic TCM telemedicine systems. Chapter 1 - Today, the main effort is focused on the optimization of different processes in order to reduce and provide the optimal consumption of available and limited resources. Conventional methods such as one-variable-at-a-time approach optimize one factor at a time instead of all simultaneously. Unlike this method, artificial neural networks provide analysis of the impact of all process parameters simultaneously on the chosen responses. The architecture of each network consists of at least three layers depending on the nature of process which to be analyzed. The optimal conditions obtained after application of artificial neural networks are significantly improved compared with those obtained using conventional methods. Therefore artificial neural networks are quite common method in modeling and optimization of various processes without the full knowledge about them. For example, one study tried to optimize consumption of electricity in electric arc furnace that is known as one of the most energy-intensive processes in industry. Chemical content of scrap to be loaded and melted in the furnace was selected as the input variable while the specific electricity consumption was the output variabl

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