Artificial Neural Networks For Engineers And Scientists
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Artificial Neural Networks for Engineers and Scientists
Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.
Artificial Neural Networks for Engineers and Scientists
Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.
Neural Networks in Bioprocessing and Chemical Engineering
Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book.Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclatureIncludes a PC-compatible disk containing input data files for examples, case studies, and practice problemsPresents 10 detailed case studiesContains an extensive glossary, explaining terminology used in neural network applications in science and engineeringProvides examples, problems, and ten detailed case studies of neural computing applications, including:Process fault-diagnosis of a chemical reactorLeonardKramer fault-classification problemProcess fault-diagnosis for an unsteady-state continuous stirred-tank reactor systemClassification of protein secondary-structure categoriesQuantitative prediction and regression analysis of complex chemical kineticsSoftware-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessingQuality control and optimization of an autoclave curing process for manufacturing composite materialsPredictive modeling of an experimental batch fermentation processSupervisory control of the Tennessee Eastman plantwide control problemPredictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems