Theoretical and experimental aspects of supervised learning in artificial neural networks

Thesis/Dissertation · Sun Jan 01 04:00:00 EST 1989

OSTI ID:5923729

Heileman, G L

The topic of supervised learning within the conceptual framework of artificial neural network (ANN) models is addressed. An ANN is a parallel distributed processing system that consists of many computationally simple processing elements interconnected through uni-directional weighted connections. Such networks, which are roughly patterned after biological nervous systems, have been proposed for use in areas in which the traditional von Neumann computer architecture has been relatively unsuccessful. Learning in these networks is accomplished through the use of algorithms that adjust the values of the connection weights. The work presented here addresses the issue of improving the rate at which ANNs can learn to achieve the mapping of an input pattern to a desired output pattern. The most successful learning algorithms for accomplishing this task are based on gradient descent error minimization techniques. However, the large amount of training time that such algorithms require is currently one of the factors that limits the use of ANN systems. A number of improvements to existing algorithms were developed and analyzed through the use of simulation. Furthermore, a highly flexible simulation environment based on an object-oriented software paradigm was developed and successfully used to implement and test these novel learning algorithms. A comparison of this object-oriented approach with the traditional techniques used by existing ANN simulation systems is performed. Finally, the issue of simulating ANN models on parallel hardware is considered.

• Research Organization: University of Central Florida, Orlando, FL (USA)
• OSTI ID: 5923729
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990200* -- Mathematics & Computers
ALGORITHMS
ARRAY PROCESSORS
ARTIFICIAL INTELLIGENCE
COMPUTERIZED SIMULATION
DATA PROCESSING
DISTRIBUTED DATA PROCESSING
ERRORS
IMPLEMENTATION
LEARNING
MATHEMATICAL LOGIC
MEMORY DEVICES
MINIMIZATION
NEURAL NETWORKS
PARALLEL PROCESSING
PERFORMANCE
PROCESSING
PROGRAMMING
SIMULATION
TRAINING