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A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

- Sayadi, Ahmad, Monjezi, Masoud, Talebi, Nemat, Khandelwal, Manoj

  • Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
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  • Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.

A dynamic surface controller based on adaptive neural network for dual arm robots

- Le, Hai, Nguyen, Linh, Thiyagarajan, Karthick

  • Authors: Le, Hai , Nguyen, Linh , Thiyagarajan, Karthick
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 555-560
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  • Description: The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.
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