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look for superior solutions to boost profitability and productivity. In recent times, artificial neural networks have demonstrated promising;
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Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different;
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Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex;
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The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence;
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Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems;
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in chromatography and spectroscopy; heterogeneous neural networks and tuning differential evolution for artificial neural networks.;
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artificial neural networks in environmental sciences and chemical engineering; application of artificial neural networks in the development;
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Artificial neural networks (ANNs) are computing systems that are motivated by the design and functioning of biological neural networks. Its;
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learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the;
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constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial;
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This book presents a broad overview on the current various design modules and applications of artificial neural systems. Artificial neural;
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cluster; fuzzy-based controller for improvement of network performance; multi-layer perceptron artificial neural network; fault detection and;
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Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;
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. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression;
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machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic;
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-layered learning network based on simple math rules. The structure of an artificial neural network consists of an input layer, an output layer and;
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literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications;
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and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing;
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of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions;
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. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the;
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; artificial neural network for biomedical image processing; machine learning in vision and robotics; system identification, process control, and;
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liquidity risk using artificial neural networks, which has been oriented in focused delay and recurrent neural networks due to their capability to;
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Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep;
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brain. ANNs have been attracting great interest during the last decade as predictive models and pattern recognition. Artificial neural networks;
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intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications;
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intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications;
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