Counterpropagation neural networks for fault detection and diagnosis

This paper shows the application of a counterpropagation neural network (CPNN) to detect single faults and their magnitudes. The performance of CPNN has been evaluated by considering a variety of faults occurring in a nonisothermal continuous stirred tank reactor (CSTR). The results presented here indicate that CPNN provides an attractive alternative to error-back-propagation (EBP) networks due to its faster learning ability for fault detection and diagnosis.

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