4 April 1997 System-based intelligent control applied to electronic test generation and diagnostics using fuzzyART and genetic algorithms
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Abstract
This paper summarizes the work that was done to explore the use a system based approach to generate test vectors for electronic systems. The process that was developed takes advantage of an unsupervised neural network algorithm Adaptive Resonance Theory (ART) and a Genetic Algorithm (GA) that is combined to form an optimal control system. The GA generates a population of test patterns (individuals). Each individual is provided as timed inputs to a set of behavior based simulations representing good and faulty circuits. The response of each model is recombined in the form of an image matrix with each row representing a signature of each of the different circuits. FuzzyART provides a method of image recognition, extracting those images that are distinctly different from any other. Each individual generated by the GA is evaluated and a fitness is provided by FuzzyART by the number of neuron clusters formed. New test sequences evolve with increasing fault isolation and detection. The process is repeated until a maximum number of models have been identified and separated. A selective breading algorithm was included to reduce the need for large populations, thus increasing the speed to converge to the `best test.'
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven Michael Singer, Steven Michael Singer, } "System-based intelligent control applied to electronic test generation and diagnostics using fuzzyART and genetic algorithms", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271539; https://doi.org/10.1117/12.271539
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