关键词: BP神经网络, 　工况识别, 　KPCA, 　遗传算法, 　粒子群算法

Abstract: The loop pipe apparatus are used to simulate the different conditions of the actual pipeline and denoise the original signal by the wavelet method. Kernel⁃based Principal Component Analysis (KPCA) is used to extract the time⁃frequency domain eigenvalues of the leaked signals, and the final input vector of the neural network is obtained. Because the traditional BP neural network is easy to fall into local minimum when it is used to identify working conditions, the BP neural network is optimized by genetic algorithm (GA) and particle swarm optimization (PSO). Compared with the traditional BP neural network,the result show that the two optimized BP neural networks have stronger ability to identify leakage working conditions. Finally, from the two aspects of test accuracy and training time, two different optimization algorithms are compared and their different application situations are proposed.

Key words: BP neural network, Condition recognition, KPCA, Genetic algorithm, Particle swarm optimization (PSO) algorithm