Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training

IEEE Transactions on Industrial Electronics

Volumn 56, Issue 8, 2009, Pages 3253-3263

  Moreno, Féilx ^a   Alarcon, Jaime ^b   Salvador, Rubén ^a   Riesgo, Teresa ^a  

^a CENTRO DE ELECTRÓNICA INDUSTRIAL   (Spain)

^b SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

Neural network hardware implementation; Recognition; Run time learning

Indexed keywords

APPLICATION-SPECIFIC DEMANDS; BACKPROPAGATION LEARNING; DYNAMIC RE-CONFIGURATION; EXPANDABILITY; HARDWARE ARCHITECTURE; LEARNING ABILITIES; NEURAL NETWORK HARDWARE IMPLEMENTATION; ONLINE TRAINING; PERCEPTRON NETWORK; PERFORMANCE BENEFITS; PROCESSING STEPS; RECOGNITION; RECONFIGURABLE HARDWARES; ROBOT CONTROLS; RUN-TIME LEARNING; RUNTIMES; SECURITY APPLICATION; SHAPE RECOGNITION; SIMULATION RESULT; SYSTEM ARCHITECTURES; WEIGHT MODIFICATIONS;

DYNAMIC MODELS; EDUCATION; HARDWARE; IMAGE RECOGNITION; NEURAL NETWORKS;

ARCHITECTURAL DESIGN;

EID: 68449089412     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2009.2022076     Document Type: Article

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