Contact State Classification in Industrial Robotic Assembly Tasks Based on Extreme Learning Machine

In industrial robotic assembly process, the work surrounding environment is generally described by Contact State (CS). In order to solve the issue of contact state recognition, contact state classification based upon Extreme Learning Machine (ELM) is proposed in industrial robotic assembly process and extended the neural network to kernel learning in this paper. ELM has excellent performance in accuracy and speed of classification, owing to node generation being independent of training data, and ELM with kernel (ELM-kernel), based on the basic classifier ELM, is applied to classify contact state of the complex assembly process. Results have shown that contact state can be recognized by the proposed classification method and the classification performance of ELM-kernel is superior to ELM. In this way, more accurate information of contact states can be provided for the robot favoring the assembly tasks.

[1]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[2]  Min Han,et al.  Partial Lanczos extreme learning machine for single-output regression problems , 2009, Neurocomputing.

[3]  Nasser L. Azad,et al.  Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification , 2014, Neurocomputing.

[4]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[5]  Ling Tang,et al.  A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting , 2015, Int. J. Inf. Technol. Decis. Mak..

[6]  Benoît Frénay,et al.  Using SVMs with randomised feature spaces: an extreme learning approach , 2010, ESANN.

[7]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[8]  C. R. Rao,et al.  Generalized Inverse of Matrices and its Applications , 1972 .

[9]  Qinghua Zheng,et al.  Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[10]  Kar-Ann Toh,et al.  Deterministic Neural Classification , 2008, Neural Computation.

[11]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[12]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[13]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[14]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[15]  Jianhui Wang,et al.  Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders , 2016 .

[16]  Baihai Zhang,et al.  Improved Meta-ELM with error feedback incremental ELM as hidden nodes , 2017, Neural Computing and Applications.

[17]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[18]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[19]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Yuh-Jye Lee,et al.  RSVM: Reduced Support Vector Machines , 2001, SDM.

[21]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[22]  Qing He,et al.  Extreme Support Vector Machine Classifier , 2008, PAKDD.

[23]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.