Conference proceeding
Neural network system for purposeful behavior based on foveal visual preprocessor
Proceedings of SPIE, v 2904(1), pp 256-262
01 Jan 1996
Abstract
Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.
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Details
- Title
- Neural network system for purposeful behavior based on foveal visual preprocessor
- Creators
- Alexander V Golovan - Southern Federal UniversityNatalia A Shevtsova - Southern Federal UniversityArkadi A Klepatch - Southern Federal University
- Publication Details
- Proceedings of SPIE, v 2904(1), pp 256-262
- Conference
- Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling
- Publisher
- Society of Photo-Optical Instrumentation Engineers (SPIE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Neurobiology and Anatomy
- Web of Science ID
- WOS:A1996BG67H00028
- Other Identifier
- 991021899311004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Optics