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Since the 1980s, the field of neural networks has undergone exponential growth. Robots in manufacturing, mining, agriculture, space and ocean exploration, and health sciences are just a few examples of the challenging applications where human-like attributes such as cognition and intelligence are playing an important role. Neural networks and related areas such as fuzzy logic and soft-computing in general are also contributing to complex decision-making in such fields as health sciences, management, economics, politics, law, and administration. In the future, robots could evolve into electro-mechanical systems with cognitive skills approaching human intelligence.

With a fascinating blend of heuristic concepts and mathematical rigor, Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory outlines the basic concepts behind neural networks and leads the reader onward to more advanced theory and applications. Pedagogically sound and clearly written, this text discusses:

• Neuronal morphology and neuro-computational systems
• Threshold logic, adaptation, and learning
• Static neural networks-MFNNs, XOR Neural Networks, and Backpropagation Algorithms
• Dynamic neural networks-both continuous-time and discrete-time
• Binary neural networks, feedback binary associative memories, fuzzy sets, and other advanced topics

Thoroughly surveying the many-faceted and increasingly influential field of neural networks, this is a valuable reference for both practitioner and student.