The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectural and learning schemes. "Artificial Neural Networks: Learning Algorithms, Performance Evaluation and Applications" presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization and the ultimate goals of the reader. A specialist should find in this book well-defined and easily reproducible algorithms which perform the training of neural networks much faster than existing algorithms, along with the performance evaluation of various neural network architectures and training schemes. "Artificial Neural Networks" can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way.The presentation of the materials in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decade is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, the book presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. The text is a valuale source of information to all researchers and engineers interested in neural networks.The book may also be used as a text for an advanced course on the subject.

The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectural and learning schemes. "Artificial Neural Networks: Learning Algorithms, Performance Evaluation and Applications" presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization and the ultimate goals of the reader. A specialist should find in this book well-defined and easily reproducible algorithms which perform the training of neural networks much faster than existing algorithms, along with the performance evaluation of various neural network architectures and training schemes. "Artificial Neural Networks" can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way.The presentation of the materials in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decade is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, the book presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. The text is a valuale source of information to all researchers and engineers interested in neural networks.The book may also be used as a text for an advanced course on the subject.

The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectural and learning schemes. "Artificial Neural Networks: Learning Algorithms, Performance Evaluation and Applications" presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization and the ultimate goals of the reader. A specialist should find in this book well-defined and easily reproducible algorithms which perform the training of neural networks much faster than existing algorithms, along with the performance evaluation of various neural network architectures and training schemes. "Artificial Neural Networks" can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way.The presentation of the materials in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decade is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, the book presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. The text is a valuale source of information to all researchers and engineers interested in neural networks.The book may also be used as a text for an advanced course on the subject.