关键词: 人工神经元网络;信息压缩＋;网络结构;液体推进剂火箭发动机;故障检测

Abstract: Due to the shortcomings of three layered forward nearal network,a four layered forward neural network model was constructed based on the information compression principle,and this network hierarchy was used to build a start up plus main stage model for parts of the parameters of liquid rocket engine system.The time series of the input parameters were used to smooth and filter as well as predict themselves.which were required in the health monitoring system for rocket engine.Thus the robustness of the health minitoring system to the measurement and process noises as well as the sensor faults were greatly improved.Evolution Strategies were used to train the weight and coefficients of the activation functions.A procedural training strategy was applied in the optimization of the huge 4 layered network model in which the network was initially trained globally in a whole,then broke down into layers for locally training.In addition,as this network hierarchy has less weight and hidden nodes than the conventional 3 layered forward network model,it is a classical network hierarchy for huge forward network model for function approximation.

Key words: Artificial neural networks;Information compression +;Littice structure;Liquid propellant rocket engine;Fault detection