Self-organizing neural architectures are described for the visual perception of static and moving forms, for autonomous real-time learning of multidimensional associative maps, and for adaptive control of variable-speed multi joint motor trajectories. Motion filtering and segmentation are carried out by a motion Boundary Contour System. Associative learning is accomplished by a Vector Associative Map, which provides an on-line alternative to the off-line properties of Back Propagation for error-based learning. Trajectory formation is carried out by a Vector Integration to Endpoint model.


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    Titel :

    Self-Organizing Neural Architectures for Vision, Learning, and Robotic Control


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    1990-01-01


    Format / Umfang :

    4 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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