C语言中递归函数的应用

该文对C语言递归函数的定义及调用进行了分析,就递归函数的应用以例题的形式进行了详细的讲解,便于初学者掌握递归函数分析思路与方法。

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.

Application of Neural Networks for Monitoring Mechanical Defects of Rotating Machines

Good monitoring of the deterioration in rotating machinery can result in reduced maintenance costs by minimizing the loss of production due to the number of machine breakdown and decreasing in the number of spare parts. In the present paper, a prognostic method based on recurrent neural networks is applied to forecast the rate of machine deterioration. Promising results have been obtained through the application of this method to the prediction of vibration based fault trends of an auxiliary gearbox of a power generation plant. This method evaluates also the seriousness of damage caused by faults.

Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.

New Generalizations of Migdal-Kadanoff Bond-Moving Recursion Procedures and Their Applications

Considering in symmetrical half-length bond operations,we present in this paper two types of newlydeveloped generalizations of the remarkable Migdal-Kadanoff bond-moving renormalization group transformation recursion procedures.The predominance in application of these generalized procedures are illustrated by making use of them to study the critical behavior of the spin-continuous Gaussian model constructed on the typical translational invariant lattices and fractals respectively.Results such as the correlation length critical exponents obtained by these means are found to be in good conformity with the classical results from other previous studies.

Fast Affine Projection Algorithm for Adaptive Noise Canceling and Its Application on the Fetal Electrocardiogram Extraction

Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.