Main description:

Natural Intelligence Neuromorphic Engineering provides readers with the most recent advances in deep learning, computational intelligence and Artificial Neural Networks (ANN), presenting detailed research and explanations of the physics and physiology principles used in developing natural intelligence for unsupervised learning of Blind Sources Separation (BSS). Author Harold Szu, a world-renowned pioneer in natural intelligence development, assembles a team of experts that cover the latest trends in deep learning, including sections on how to improve robust internal knowledge representation, big database data mining, and real time optical flow.

This collaborative work offers researchers and graduate students the most up-to-date information on the theories and key applications in natural intelligence and deep learning towards real-time, error-free and automatic target recognition.

Contents:

1. Rule-Based Artificial Intelligence versus Artificial Neural Network Learning (ANN) Using Hinton and Jordan Deep Learning 2. Theorem of Natural Intelligence (NI): Necessary and Sufficient Conditions for D.O. Hebb Unsupervised Learning Rule 3. Improving Deep Learning through Associative Memory Expert Systems, Multiple layer Deep Learning, Compressive Sensing, Capture Novelty Detection 4. Traditional ANN, Neural Dynamics, and the Lyapunov Convergence Theorem 5. Stochastic Divide and Conquer by Fast-Simulated Annealing Searching of the Global Minimum 6. ANN Smart Sensors and Human Visual Systems Automation to the Industry 7. Biological Chaotic Neural Networks Modeling and VLSI Implementations 8. Fuzzy Logic with Possibility versus Probability Membership Functions 9. ANN Pattern Recognition and Aided Target Recognition 10. Ear-like Adaptive Wavelet Processing with Szu's Super-Mother Wavelet Theorem 11. ANN Financial Analyses 12. How Smartphones with Big Databases Analysis ANN Can Help Public Health 13. ANN Smartphone with MEMS Smart Nodes can Nowcast Earthquakes