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Soft Computing

Soft Computing. Lecture 21 Review of using of NN for solving of real tasks. Autopilot airplane. LoFLYTE (Low-Observable Flight Test Experiment) is developed for NASA and USA Air Force by Accurate Automation Corp., Chattanooga, TN. Speed is 4-5 M

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Soft Computing

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  1. Soft Computing Lecture 21 Review of using of NN for solving of real tasks

  2. Autopilot airplane • LoFLYTE (Low-Observable Flight Test Experiment)is developed for NASA and USA Air Force by Accurate Automation Corp., Chattanooga, TN. • Speed is 4-5 M • NN is learning by pilot (storing associations between situation and action of pilot) and after that may control without pilot

  3. Project TNA • System for searching of plastic explosive in baggage in airports is developed by SAIC (Science Application International Corporation) • NN analyzes of spectrum of baggage after irradiation of it by slow neutrons • Recognition of explosive with probability 97%, speed is 10 units per minute

  4. Using of NN on financial markets • Citibank uses NN from 1990. In 1992 yield was 25% which is large more then most of brokers • Chemical Bankuses NN (from company Neural Data) for previous processing of transactions in currency exchanges in 23 countries for detection of shady bargains • Fidelity of Bostonuses NN for control of portfolio with volume 3 billion USA, Deere & Co– 100 million USA, LBS Capital– 400 million USA • Proceedings of one seminar “AI in Wall Street” includes 6 large volumes.

  5. Recognition of stolen credit cards • Developer is HNC Software Corp., now Fair IsaacCorporation http://www.fairisaac.com • Software Falconbased on NN • This company controls more then 220 million accounts • NN is learning to recognize unusual behavior of clients with credit card

  6. Active reclaim in Internet • Developer is Aptex Software Inc. • Software SelectCastbased on NN • NN is learning by interesting of users and offers to client such reclaim which may be interesting for him

  7. Control of mobile robots • LSTM for robots • Planning of path and navigation • Recognition of objects

  8. Camera-robot coordination is function approximation • The system we focus on in this section is a work floor observed by a fixed cameras and a robot arm. The visual system must identify the target as well as determine the visual position of the end-effector.

  9. Camera-robot coordination is function approximation (2)

  10. Camera-robot coordination is function approximation (3).Two approach to use neural networks: • Usage of feed-forward networks • Indirect learning • General learning • Specialized learning • Usage of topology conserving maps

  11. Camera-robot coordination is function approximation (4). feed-forward networks Indirect learning system for robotics. In each cycle the network is used in two different places: first in the forward step then for feeding back the error

  12. Camera-robot coordination is function approximation (5). feed-forward networks (2)

  13. Sensor based control

  14. The structure of the network for the autonomous land vehicle

  15. Drama

  16. Diagnosis of cancer • Yulei Jiang, assistant professor of radiology at the University of Chicago • System that uses a perceptron neural network to analyze eight input nodes, converting the output node into a robability measure, which Jiang calls likelihood of malignancy. • The system is trained on a set of cases, using the leave-one-out method, which is a cross-validation technique for estimating generalization error based on resampling. A net is trained a number of times, each time leaving out one of the subsets. The omitted subset computes whatever error criterion is of interest. The system initially learns from digitized screen mammograms. • The eight input nodes represent features of calcifications, areas in breast tissue where tiny calcium deposits build up and might indicate the presence of cancer.

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