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Natural Intelligence Neuromorphic Engineering

  • ID: 4753522
  • Book
  • September 2019
  • Region: Global
  • 480 Pages
  • Elsevier Science and Technology
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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.

  • Covers natural intelligence uses in today's fast-advancing computational intelligence applications
  • Features MATLAB codes in each chapter that will be made available as free downloads for readers
  • Provides a short and concise explanation of the physics and physiological principles necessary for developing natural intelligence through unsupervised learning and blind sources separation (BSS)
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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

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Szu, Harold
Dr. Szu has been a champion of components of human sciences (http://www.ica-wavelet.org) and brain-style computing for 2 decades;

he received the INNS D. Gabor Award in 1997 and the Eduardo R. Caianiello Award in 1999 from the Italy Academy.

Recently, he contributed to the unsupervised learning theory of the thermodynamic free energy of sensory pair for fusion. Besides 440 publications,

(cf. https://www.researchgate.net/profile/Harold_Szu2) over dozen US patents, numerous books & journals, conference proceedings.

Dr. Szu taught students "how to be creative in interdisciplinary sciences” according to the Reinsurance Individual and Team Creativity Methodology,

and guided over a dozen PhD students. (http://www.genealogy.math.ndsu.nodak.edu/id.php?id=44103).

He received a Ph.D. in Theoretical Physics from G.E. Uhlenbeck of the Rockefeller Univ., New York, NY.

He began at NRL, NSWC, ONR, and now a senior scientist at Army Night Vision Electronic Sensor Director, Ft. Belvoir, VA.

Since CUA has campus on Ft. Belvoir, Prof. Szu left GWU and is appointed as Professor of CUA (http://biomedical.cua.edu/faculty-staff/Szu.cfm)

. Fellow of AIMBE 2004 for breast cancer passive spectrogram diagnoses.

. Fellow of IEEE (1997) for bi-sensor fusion;

. Foreign Academician, RAS 1999, for unsupervised learning.

. Fellow of OSA (1996) for adaptive wavelet

. Fellow of SPIE since 1995 for neural nets.

. Fellow of INNS (2010) for a founder and former president

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