Automated Identification of Basic Control Charts Patterns Using Neural Networks

218

cess to identify a wide range of patterns is high and com-

parable if not superior to the previous reported results.

This proves that changing the network structure and us-

ing a compatible training algorithm with the network

structure has a great effect on the network performance.

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