This self-contained introductory text explains the basic principles of computing with models of artificial neural networks, which the students with a background in basic engineering or physics or mathematics can easily understand. Besides students, practising engineers and research scientists would also cherish this book which treats the emerging and exciting area of artificial neural networks with the following distinguishing features:
KEY FEATURES
• Principles of neural networks are explained without presuming any prior knowledge of the subject.
• While pattern processing features of the neural networks are emphasised, the pattern recognition tasks used in problem solving by human beings are identified.
• Analysis of pattern recognition tasks are presented in detail by basic topologies of artificial neural networks.
• Includes real-world applications of neural networks in speech and image processing.
• The text discusses the following topics from first principles:
– Activation and synaptic dynamics
– Learning laws for feedforward neural networks
– Analysis of feedback neural networks
– Competitive learning networks
– Architectures for complex pattern recognition tasks
– Applications in speech and image processing.
KEY FEATURES
• Principles of neural networks are explained without presuming any prior knowledge of the subject.
• While pattern processing features of the neural networks are emphasised, the pattern recognition tasks used in problem solving by human beings are identified.
• Analysis of pattern recognition tasks are presented in detail by basic topologies of artificial neural networks.
• Includes real-world applications of neural networks in speech and image processing.
• The text discusses the following topics from first principles:
– Activation and synaptic dynamics
– Learning laws for feedforward neural networks
– Analysis of feedback neural networks
– Competitive learning networks
– Architectures for complex pattern recognition tasks
– Applications in speech and image processing.
