A method for fault diagnosis in chemical reactor with hybrid neural network and genetic algorithm

Rapid and accurate fault diagnosis remains a problem in the case of multiple fault for the large and complex chemical system. A novel evolutionary neural network for fault diagnosis is suggested. Which adopts three-layer feed - forward neural network with dual genetic algorithm (GA)loops embedded in its training. The dual GA loops are designed for optimizing both topology and connection weights of the neural network and establishing global optimal neural network for fault diagnosis. Computer simulation results in a chemical reactor indicate that the proposed evolutionary neural network fault diagnosis system works effectively and is superior to the conventional back propagation(BP)neural network.