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Intelligent Information Systems

Intelligent Information Systems. Prof. M. Muraszkiewicz Institute of Information and Book Studies Warsaw University mietek@n-s.pl. Neural Nets Module 10. Table of Contents. Background Historical Note Definition Properties and Applications. Background. Two Tracks in AI.

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Intelligent Information Systems

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  1. Intelligent Information Systems Prof. M. Muraszkiewicz Institute of Information and Book Studies Warsaw University mietek@n-s.pl M. Muraszkiewicz

  2. Neural Nets Module 10 M. Muraszkiewicz

  3. Table of Contents • Background • Historical Note • Definition • Properties and Applications M. Muraszkiewicz

  4. Background M. Muraszkiewicz

  5. Two Tracks in AI Analytical, symbolic Invented by researchers (inspired by logics and math – J. von Neumann). „Naturalistic” Based on solutions worked out by “mother nature” through evolution (inspired by psychology, neurology, biology, evolution –K. Darwin, ...). M. Muraszkiewicz

  6. About the Human Brain “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” Emmerson M. Pough M. Muraszkiewicz

  7. Parameters • volume: ~1400 cm3, • weight: ~1,5 kG, • surface: ~2000 cm2 (the surface of a sphere of the same volume is ~ 600 cm2), • ~ 1010neurons, • 1012glia cells, • number of connections - ~ 1015average length from 0,01 mm to 1m. • Neurons receive and send impulses whose frequency is 1 - 100 Hz, duration 1 - 2 ms, voltage 100 mV andspeed of propagation 1 - 100 m/s. • Speed of brain – 1018operations/s (parallel processing). • Informational capacity of senses’ channels: -- vision - 100 Mb/s, -- touch - 1 Mb/s, -- audition - 15 Kb/s, -- smell - 1 Kb/s, -- taste - 100 b/s. (source R. Tadeusiewicz, „Sieci neuronowe”). M. Muraszkiewicz

  8. Historical Note M. Muraszkiewicz

  9. Difficult History • W. McCulloch, W. Pitts – first mathematical model of a neuron (1943), • D. Hebb – the rule that determines the change in the weight connection, • F. Rosenblatt’s Perceptron (1957), a two-layer network,for recognizing alphanumerical characters, • B. Widrow, M. Hoff – ADALINE • M. Minsky (1969) – proved limits of simple neural nets which weakened research in the 70’ies, • J. Hopfield’s Netwith a feedback (1982), • Works by J. Andersona (1988) – neural nets’ “comeback". Warren McCulloch 1898-1969 M. Muraszkiewicz

  10. Definition M. Muraszkiewicz

  11. Intuitive Definition “A neural network is a set of simple processors (“neurons”) connected in a certain way. A neuron can have many inputs (synapses) with which weights can be associated. The value of weights can be changed during the operation of a network to produce the desired data flow within it what makes the network and adaptive device. Topology of the network and the values of weights determine the program executed on the network. M. Muraszkiewicz

  12. Definition from Wikipedia “An artificial neural network (ANN), often just called a "neural network" (NN), is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation” http://en.wikipedia.org/wiki/Artificial_neural_network M. Muraszkiewicz

  13. Types of Nets The neurons learn in an iterative way. By adding an error detector and a feature to change weights simple nets become to new models such as ADALINE (ADAptive LINear Element). M. Muraszkiewicz

  14. Properties and Applications M. Muraszkiewicz

  15. Main Properties Advantages • adaptiveness and self-organization • parallel processing, • learning (supervised and unsupervised) • fault tolerance Disadvantages • non-explicability • slow M. Muraszkiewicz

  16. Type of Applications • prediction • optimization • classification • pattern and sequence recognition • data analysis and association, • filtering • ... M. Muraszkiewicz

  17. Examples of Applications • Speech analysis • Planning of learning progress • Analysis of production problems • Trade activities optimization • Spectral analysis • Optimization of wastes utilization • Selection of row materials • Forensic support • Staff recruitment support • Industrial processes control • ... • Diagnostics of electronic devices • Psychiatric research • Stock exchange predictions • Sales predictions • Search for oil fields • Interpretation of biological research • Prices prediction • Analysis of medical data • Planning of machines maintenance M. Muraszkiewicz

  18. Readings • Haykin S., “Neural Networks: A Comprehensive Foundation” (3rd Edition), Prentice Hall, 2007. • Lawrence, J., “Introduction to Neural Networks”, California Scientific Software Press, 1994. • Royas R., “Neural Networks: A Systematic Introduction”, Springer, 1996. http://en.wikipedia.org/wiki/Neural_networks http://en.wikipedia.org/wiki/Artificial_neural_network M. Muraszkiewicz

  19. M. Muraszkiewicz

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