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Neural Network Learning: Algorithms and Expert Systems by Jesús Sopko
English | 2022 | ISBN: 1682516997 | 288 pages | PDF | 13 Mb
A neural network is a complex structure which consists of a group of interconnected neurons which provides very exciting alternatives for complex problem solving and other applications which can play an important role in today's computer science field, so researchers from different disciplines are designing artificial neural networks to solve the problems of pattern recognition, prediction, optimization, associative memory and control. Expert systems have been used widely in many areas and industries. Expert Systems are developed with the intention of making the system have the same level of expertise as a trained professional in the field for which the system is built. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of decisions, more consistent decisions, and a shorter decision-making process. The remarkable achievement applications of AI have been reported in different disciplines including the fields of medicine, military, chemistry, engineering, manufacturing, management, and others.
The book presents a wide-ranging coverage of neural network learning algorithms and neural network expert systems. It addresses how expert system techniques are being used in problem solving and why someone in operational research might want to use them. Currently, artificial neural networks are the clustering of primitive artificial neurons. This clustering occurs by creating layers which are then connected to one another. How these layers connect is the other part of the "art" of engineering networks to resolve the complex problems of the real world. This book brings together key experimental and theoretical ANN research with the aim of providing new insights on information processing in biological neural networks through the use of artificial neural networks. Because of recent advances in neural network technology, hybrid intelligent systems can now address new problems. As these systems grow in number and importance, developers need a framework for understanding the combinations of neural networks and expert systems and will need models and guidelines for effective implementation. It discusses expert system technology and its development methodologies and discusses how machine learning factors in each of the methodologies. This book presents a survey of neural network applications in real-world scenarios. It provides a taxonomy of artificial neural networks (ANNs) and furnishes the reader with knowledge of current and emerging trends in ANN applications research and areas of focus for researchers. Additionally, the book presents ANN application challenges, contributions, compares performances and critiques methods.
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