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Biological oscillatory activity in neural networks has been intensively studied over the past years. Neuronal oscillations are the basis of many different behavioral patterns and sensory mechanism. Understanding the dynamic properties of these mechanisms is useful for analyses of biological functions and medical diagnoses. The dynamic characteristics of wake‐sleep circadian rhythms and ECG’s cardiac cycle data measured for normal subjects are identified here, using MA‐BP neural network model. It was found that dynamics of regular components can be captured by the model. The captured dynamics are kept in a steady state for some periods. The order of the MA neural network was suppressively controlled by the first 2∼3 orders. Hence it may be useful for medical diagnoses of circadian rhythms and heart related diseases.

It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine.

This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine.

The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples.

The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion.

The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.