Comparison of extreme learning machine and neural network method on hand typist robot for quadriplegic person

On this paper, the predicted results of the two types of methods will compare. This is very important because, the error in the determination method will cause the prediction result is not optimal. Two types of methods to be coordinated are the Extreme Learning Machine and Back propagation Neural Network. Testing will be done by entering the calculation from each method to Hand Typist Robot to perform the same task. This robot is used to help Quadriplegic Person in operating a computer keyboard and has 4 Degrees of Freedom (DOF) in both arms. The actuator for all DOFs is Dynamixel AX-12A. This robot processed CMPS10 sensor data which sense the changes provided by Quadriplegic Person. The change of motion will be processed using a method to predict the movement of each Dynamixel AX-12A motor. The most optimal method will produce the lowest Mean Square Error (MSE).

[1]  Adi Soeprijanto,et al.  Real-time unbalanced load flow development using direct-ZBR method and modified lambda iteration for on-line monitoring and control , 2013 .

[2]  Adi Soeprijanto,et al.  Incremental Particle Swarm Optimizer with local search for Optimal Power Flow Subjected to Digital GCC based on Neural Network , 2012 .

[3]  Juliana Johari,et al.  Water storage monitoring system with pH sensor for pharmaceutical plants , 2016, 2016 6th International Conference on System Engineering and Technology (ICSET).

[4]  Deniz Korkmaz,et al.  Extreme learning machine based robotic arm modeling , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[5]  Patrick Henkel,et al.  Calibration of magnetic field sensors with two mass-market GNSS receivers , 2014, 2014 11th Workshop on Positioning, Navigation and Communication (WPNC).

[6]  Patricio Reyes-Bedoya,et al.  BRACON: Control system for a robotic arm with 6 degrees of freedom for education systems , 2015, 2015 6th International Conference on Automation, Robotics and Applications (ICARA).

[7]  Adi Soeprijanto,et al.  Digital Generator Capability Curve for Improving Optimal Power Flow based on IPSO , 2013 .

[8]  C. L. Liu,et al.  Robust microgrid power flow using particle swarm optimization , 2013, International School on Nonsinusoidal Currents and Compensation 2013 (ISNCC 2013).

[9]  Adi Soeprijanto,et al.  Comparison methods of Fuzzy Logic Control and Feed Forward Neural Network in automatic operating temperature and humidity control system (Oyster Mushroom Farm House) using microcontroller , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).

[10]  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).

[11]  Adi Soeprijanto,et al.  Smart-Meter based on current transient signal signature and constructive backpropagation method , 2014, 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering.

[12]  Jianhua Wang,et al.  The control of Dynamixel RX-28 based on VC++ for the locomotion of cockroach robot , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[13]  Adi Soeprijanto,et al.  Rotor bars fault detection by DFT spectral analysis and Extreme Learning Machine , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).

[14]  Adi Soeprijanto,et al.  Optimal Placement and Sizing of Distributed Generation for Minimize Losses in Unbalance Radial Distribution Systems Using Quantum Genetic Algorithm , 2014 .

[15]  Kuo Lung Lian,et al.  Improved Robustness of Sequential Three Phase Power Flow Using Homotopic Method , 2013 .

[16]  Kuo Lung Lian,et al.  Microgrid power flow using Homotopic and Runge-Kutta Method , 2015, 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC).

[17]  Tsai-Hsiang Chen,et al.  A distribution power flow using particle swarm optimization , 2012, PES 2012.

[18]  Adi Soeprijanto.  Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method , 2010 .

[19]  Adi Soeprijanto,et al.  Neural network implementation for invers kinematic model of arm drawing robot , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).