Neural networks represent a new generation of information processing paradigms designed to mimic-in a very limited sense-the human brain. They can learn, recall, and generalize from training data, and with their potential applications limited only by the imaginations of scientists and engineers, they are commanding tremendous popularity and research interest. Over the last four decades, researchers have reported a number of neural network paradigms, however, the newest of these have not appeared in book form-until now.
Recent Advances in Artificial Neural Networks collects the latest neural network paradigms and reports on their promising new applications. World-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.
1. A Neuro-Symbolic Hybrid Intelligent Architecture With Applications 2. New Radical Basis Neural Networks and Their Application in a Large-Scale Handwritten Digit Recognition Problem 3. Efficient Neural Network-Based Methodology for the Design of Multiple Classifiers 4. Learning Fine Motion in Robotics: Design and Experiments 5. A New Neural Network for Adaptive Pattern Recognition of Multichannel Input Signals 6. Lateral Priming Adaptive Resonance Theory (LAPART)-2: Innovation in Art 7. Neural Network Learning in A Travel Reservation Domain 8. Recent Advances in Neural Network Applications in Process Control 9. Monitoring Internal Combustion Engines by Neural Network Based Virtual Sensing 10. Neural Architectures of Fuzzy Petri Nets