Publisher's Synopsis
Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers. Computers do rote things well, like keeping ledgers or performing complex math. But computers have trouble recognizing even simple patterns much less generalizing those patterns of the past into actions of the future. Now, advances in biological research promise an initial understanding of the natural thinking mechanism. Artificial neural networks have been in use for some time now and we can find them working in areas such as process control, chemistry, gaming, radar systems, automotive industry, space industry, astronomy, genetics, banking, fraud detection, etc. and solving of problems like function approximation, regression analysis, time series prediction, classification, pattern recognition, decision making, data processing, filtering, clustering, etc., naming a few. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a back propagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. Recent Advances in Artificial Neural Networks is a compilation of the latest neural network paradigms and reports on their promising new applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Furthermore, the book provide recent advances of architectures, methodologies, and applications of artificial neural networks. There are a variety of ANN architectures, such as Multilayer Feed forward Network (MFN), Radial Basis Function (RBF) network, recurrent network, feedback network, and Kohonen Self-Organizing Map (SOM) network. Well-renowned experts discuss the use of neural networks in pattern recognition, color induction, classification, cluster detection, and more. Application engineers, scientists, and research students from all disciplines with an interest in considering neural networks for solving real-world problems will find this collection useful. Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers. Computers do rote things well, like keeping ledgers or performing complex math. But computers have trouble recognizing even simple patterns much less generalizing those patterns of the past into actions of the future. Now, advances in biological research promise an initial understanding of the natural thinking mechanism. Artificial neural networks have been in use for some time now and we can find them working in areas such as process control, chemistry, gaming, radar systems, automotive industry, space industry, astronomy, genetics, banking, fraud detection, etc. and solving of problems like function approximation, regression analysis, time series prediction, classification, pattern recognition, decision making, data processing, filtering, clustering, etc., naming a few. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a back propagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. Recent Advances in Artificial Neural Networks is a compilation of the latest neural network paradigms and reports on their promising new applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Furthermore, the book provide recent advances of architectures, methodologies, and applications of artificial neural networks. There are a variety of ANN architectures, such as Multilayer Feed forward Network (MFN), Radial Basis Function (RBF) network, recurrent network, feedback network, and Kohonen Self-Organizing Map (SOM) network. Well-renowned experts discuss the use of neural networks in pattern recognition, color induction, classification, cluster detection, and more. Application engineers, scientists, and research students from all disciplines with an interest in considering neural networks for solving real-world problems will find this collection useful. Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers. Computers do rote things well, like keeping ledgers or performing complex math. But computers have trouble recognizing even simple patterns much less generalizing those patterns of the past into actions of the future. Now, advances in biological research promise an initial understanding of the natural thinking mechanism. Artificial neural networks have been in use for some time now and we can find them working in areas such as process control, chemistry, gaming, radar systems, automotive industry, space industry, astronomy, genetics, banking, fraud detection, etc. and solving of problems like function approximation, regression analysis, time series prediction, classification, pattern recognition, decision making, data processing, filtering, clustering, etc., naming a few. Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on back propagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a back propagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. Recent Advances in Artificial Neural Networks is a compilation of the latest neural network paradigms and reports on their promising new applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Furthermore, the book provide recent advances of architectures, methodologies, and applications of artificial neural networks. There are a variety of ANN architectures, such as Multilayer Feed forward Network (MFN), Radial Basis Function (RBF) network, recurrent network, feedback network, and Kohonen Self-Organizing Map (SOM) network