scholarly journals Predicting construction labor productivity using lower upper decomposition radial base function neural network

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Sasan Golnaraghi ◽  
Osama Moselhi ◽  
Sabah Alkass ◽  
Zahra Zangenehmadar
Keyword(s):  
Neural Network ◽  
Labor Productivity ◽  
Base Function ◽  
Lower Upper ◽  
Radial Base Function

Applying RBF Neural Networks and Genetic Algorithms to Nonlinear System Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 2457-2460 ◽  
Author(s):  
Hong Fa Wang ◽  
Xin Ai Xu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Rbf Neural Network ◽  
System Optimization ◽  
Rbf Neural Networks ◽  
Optimal Solutions ◽  
Base Function ◽  
The Neural Network ◽  
Engineering Practices ◽  
Radial Base Function

Nonlinear system optimization is always an issue that needs to be considered in engineering practices and management. In order to obtain optimal solutions without analysis formulas to nonlinear systems, we first construct a radial-base-function (RBF) neural network using the newrb() function in MALTAB 7.0, then train the neural network according to input and output, and finally obtain the solution using a genetic algorithm. Simulated experimental results show that the proposed algorithm is able to achieve optimal solutions with a relatively fast speed of convergence.

Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

2021 ◽  
Vol 14 ◽  
Author(s):  
Imen Saidi ◽  
Nahla Touati
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Mobile Manipulator ◽  
Control Law ◽  
Parameter Learning ◽  
Base Function ◽  
Mode Control ◽  
Radial Base Function

Background: In this paper, we have developed an intelligent control law for the control of mobile manipulator robots by investigating the various techniques proposed in the literature. Thus, we have adopted a hybrid approach that integrates a part of classical and advanced automation in order to create an efficient control structure that can cope with a certain level of complexity. Our research logic is based on the process of keeping in mind that the control system must comply with the constraints imposed during the implementation of the control architecture. Objective: This paper aims to develop a control law in order to guarantee a certain level of performance, more precisely, during a trajectory tracking application for mobile handling missions. The developed control law guarantees robustness with respect to external disturbances and parametric uncertainties due to the modelling of the system. Methods: In this paper, a study of the basic concepts of robotics and robot modelling is presented in order to set up the dynamic model used for the elaboration of the command. A sliding-mode controller based on a radial base function neural network with minimum parameter learning is developed for the Pelican robot as a two-link robot manipulator. This approach, which combines a radial base function neuronal network (RBFNN) and a sliding mode control (SMC), is presented for the tracking control of this class of systems with unknown non-linearities. The centre and output weights of the RBFNN are updated via online learning in accordance with the adaptive laws, allowing the control output of the neural network to approach the equivalent control in the sliding mode in the predetermined direction. The Lyapunov function is used to develop the adaptive control algorithm based on the RBFNN model. For reducing the computational load and increasing real-time arm performance, an RBFNN-based on the SMC with the Minimum Parameter Learning (MPL) method is designed. Results: Neural network sliding mode control is designed to underline the effectiveness of the approach to control the manipulator;—this method of control is used to ensure the tracking trajectories. Conclusion: The results of the simulation for the manipulator's arm demonstrated the effectiveness of the modelling strategy, the correction, and the robustness of the control approach.

Estimating Labor Productivity Using Probability Inference Neural Network

2000 ◽  
Vol 14 (4) ◽  
pp. 241-248 ◽  
Author(s):  
Ming Lu ◽  
S. M. AbouRizk ◽  
Ulrich H. Hermann
Keyword(s):  
Neural Network ◽  
Labor Productivity
Sign in / Sign up

Export Citation Format

Share Document