Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator.

[1]  Faramarz Gordaninejad,et al.  Performance of a new magnetorheological elastomer isolation system , 2014 .

[2]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[3]  Fang Wu,et al.  Step-wise support vector machines for classification of overlapping samples , 2015, Neurocomputing.

[4]  Faramarz Gordaninejad,et al.  Modeling of a magnetorheological elastomer-based isolator , 2014 .

[5]  Haidar Samet,et al.  A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting , 2014, Expert Syst. Appl..

[6]  X. Wen,et al.  A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region , 2014 .

[7]  Chen Jing,et al.  Fault detection based on a robust one class support vector machine , 2014, Neurocomputing.

[8]  Jianchun Li,et al.  A Highly Adjustable Base Isolator Utilizing Magnetorheological Elastomer: Experimental Testing and Modeling , 2015 .

[9]  Chunquan Li,et al.  A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller , 2012 .

[10]  Weihua Li,et al.  A highly adjustable magnetorheological elastomer base isolator for applications of real-time adaptive control , 2013 .

[11]  Xiao-li Li,et al.  Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation , 2013, Neurocomputing.

[12]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[13]  Xiaobing Kong,et al.  Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.

[14]  Bijan Samali,et al.  Development and characterization of a magnetorheological elastomer based adaptive seismic isolator , 2013, Smart Materials and Structures.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Rudrajeet Pal,et al.  Business health characterization: A hybrid regression and support vector machine analysis , 2016, Expert Syst. Appl..

[17]  Salah Bouhouche,et al.  Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process , 2010 .

[18]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[19]  Ana Madevska Bogdanova,et al.  Probabilistic SVM outputs for pattern recognition using analytical geometry , 2004, Neurocomputing.

[20]  Wen-Tsao Pan,et al.  Using modified fruit fly optimisation algorithm to perform the function test and case studies , 2013, Connect. Sci..

[21]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[22]  Weihua Li,et al.  Experimental study and modeling of a novel magnetorheological elastomer isolator , 2013 .

[23]  Goutam Saha,et al.  Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier , 2010, Expert Syst. Appl..

[24]  Shan Liu,et al.  An improved fruit fly optimization algorithm and its application to joint replenishment problems , 2015, Expert Syst. Appl..

[25]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[26]  Xiaoyu Gu,et al.  Frequency control of smart base isolation system employing a novel adaptive magneto-rheological elastomer base isolator , 2016 .

[27]  Weihua Li,et al.  A state-of-the-art review on magnetorheological elastomer devices , 2014 .

[28]  Masami Nakano,et al.  Development of a novel multi-layer MRE isolator for suppression of building vibrations under seismic events , 2016 .

[29]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[30]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[31]  Jianchun Li,et al.  Corrigendum: A highly adjustable magnetorheological elastomer base isolator for applications of real-time adaptive control (2013 Smart Mater. Struct. 22 095020) , 2014 .

[32]  Yongsheng Ding,et al.  An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism , 2015 .

[33]  Fang Wu,et al.  Steel plates fault diagnosis on the basis of support vector machines , 2015, Neurocomputing.

[34]  Xiaofang Yuan,et al.  Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm , 2015, Appl. Math. Comput..

[35]  Jianchun Li,et al.  Parameter identification and sensitivity analysis of an improved LuGre friction model for magnetorheological elastomer base isolator , 2015 .

[36]  Dong Xu,et al.  Bi-density twin support vector machines for pattern recognition , 2013, Neurocomputing.

[37]  Seyed Taghi Akhavan Niaki,et al.  An improved fruit fly optimization algorithm to solve the homogeneous fuzzy series–parallel redundancy allocation problem under discount strategies , 2016, Soft Comput..

[38]  Hao Zhou,et al.  Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization , 2012, Eng. Appl. Artif. Intell..

[39]  Kai-Tai Song,et al.  A New Information Fusion Method for Bimodal Robotic Emotion Recognition , 2008, J. Comput..