To improve the poor recognize ability of back propagation(BP) network to an unknown flatness and complexity of optimizing
the initial weight of the neural network with genetic algorithms(GA) in
strip flatness pattern-recognition,a kind of GA-BP model on the strip flatness pattern-recognition was put forward,which is based on multidimensional space genetic algorithms to optimize the initial weigh of BP network.Such a model uses network weight directly to construct multidimensional space chromosome,combines the training sample error and testing sample error together as adaptive value function,and takes higher network goal error and lowed evolvement degree as the training tactics.The results show that genetic algorithms optimizing method based on the multidimensional space makes the modeling very simple,and makes encode and decode unnecessary,and reduces the calculation amount in programming greatly.GA-BP model raises the recognition capability and precision of BP network to that of the unknown samples effectively.In industry test,the pattern recognition result of GA-BP model is very similar to the real flatness with measuring equipment.