Ebook: Radial Basis Function Networks 2: New Advances in Design

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.

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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.

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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
Content:
Front Matter....Pages i-xix
An Overview of Radial Basis Function Networks....Pages 1-36
Using Radial Basis Function Networks for Hand Gesture Recognition....Pages 37-58
Using Normalized RBF Networks to Map Hand Gestures to Speech....Pages 59-101
Face Recognition Using RBF Networks....Pages 103-141
Classification of Facial Expressions with Domain Gaussian RBF Networks....Pages 143-165
RBF Network Classification of ECGs as a Potential Marker for Sudden Cardiac Death....Pages 167-214
Biomedical Applications of Radial Basis Function Networks....Pages 215-268
3-D Visual Object Classification with Hierarchical Radial Basis Function Networks....Pages 269-293
Controller Applications Using Radial Basis Function Networks....Pages 295-317
Model-Based Recurrent Neural Network for Fault Diagnosis of Nonlinear Dynamic Systems....Pages 319-352
Back Matter....Pages 353-360

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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
Content:
Front Matter....Pages i-xix
An Overview of Radial Basis Function Networks....Pages 1-36
Using Radial Basis Function Networks for Hand Gesture Recognition....Pages 37-58
Using Normalized RBF Networks to Map Hand Gestures to Speech....Pages 59-101
Face Recognition Using RBF Networks....Pages 103-141
Classification of Facial Expressions with Domain Gaussian RBF Networks....Pages 143-165
RBF Network Classification of ECGs as a Potential Marker for Sudden Cardiac Death....Pages 167-214
Biomedical Applications of Radial Basis Function Networks....Pages 215-268
3-D Visual Object Classification with Hierarchical Radial Basis Function Networks....Pages 269-293
Controller Applications Using Radial Basis Function Networks....Pages 295-317
Model-Based Recurrent Neural Network for Fault Diagnosis of Nonlinear Dynamic Systems....Pages 319-352
Back Matter....Pages 353-360
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