An Optimized Resolution Coefficient Algorithm of Gray Relation Classifier

Resolution coefficient of traditional gray relation classifier usually takes a fixed value of 0.5, which greatly limits the adaptive ability, and reduces the effectiveness of this algorithm to identify signals. To solve this problem, an improved optimized resolution coefficient algorithm of gray relation classifier was proposed. Particle swarm optimization (PSO) algorithm was used to calculate the optimized resolution coefficient corresponding to the best classification results under different SNR environment. The adaptive ability of this algorithm was improved by improving the selection method of resolution coefficient and ultimately the classification effect was improved. Simulation results show that, compared with the traditional improved algorithm, it can improve the recognition rate of signals under different SNR environment, and have a good application value.

[1]  Xiaozhi Hu,et al.  Effects of heterogeneity and load amplitude on fatigue rate prediction of a welded joint , 2016 .

[2]  S. Cross,et al.  RAD51B in Familial Breast Cancer , 2016, PloS one.

[3]  Jian Guo,et al.  Study on rolling bearing on-line reliability analysis based on vibration information processing , 2017, Comput. Electr. Eng..

[4]  Jingchao Li.  A New Robust Signal Recognition Approach Based on Holder Cloud Features under Varying SNR Environment , 2015, KSII Trans. Internet Inf. Syst..

[5]  Jingchao Li.  A Novel Recognition Algorithm Based on Holder Coefficient Theory and Interval Gray Relation Classifier , 2015, KSII Trans. Internet Inf. Syst..

[6]  Jian Guo,et al.  Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis , 2016 .

[7]  Zhou Ruo-lin.  Application of improved grey correlation algorithm on radiation source recognition , 2010 .

[8]  Jingchao Li,et al.  Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm , 2015 .

[9]  Bernard C. Jiang,et al.  Machine vision-based gray relational theory applied to IC marking inspection , 2002 .

[10]  Yunpeng Cao,et al.  Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm , 2016 .

[11]  Kao-Shing Hwang,et al.  A Grey Synthesis Approach to Efficient Architecture Design for Temporal Difference Learning , 2011, IEEE/ASME Transactions on Mechatronics.

[12]  Meng-Hui Wang,et al.  Fuel cell fault forecasting system using grey and extension theories , 2012 .

[13]  Jingchao Li,et al.  A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory , 2016, PloS one.

[14]  G. W. Chang,et al.  Forecasting Flicker Severity by Grey Predictor , 2012, IEEE Transactions on Power Delivery.

[15]  Zhimin Zhao,et al.  Grey theory applied in non-subsampled Contourlet transform , 2012 .

[16]  Jian Guo,et al.  A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals , 2015 .

[17]  Okyay Kaynak,et al.  A Grey System Modeling Approach for Sliding-Mode Control of Antilock Braking System , 2009, IEEE Transactions on Industrial Electronics.