S
Serban Olaru
Researcher at Politehnica University of Bucharest
Publications - 39
Citations - 167
Serban Olaru is an academic researcher from Politehnica University of Bucharest. The author has contributed to research in topics: Robot & Instrumentation (computer programming). The author has an hindex of 7, co-authored 38 publications receiving 153 citations.
Papers
More filters
Journal ArticleDOI
Assisted Research of the Neural Network
TL;DR: The general components and the mathematical model of some more important neurons and one numerical simulation of the linear neural network are shown and the least mean square (LMS) error algorithm is used for adjusting the weights and biases and incremental training by different training rate.
Journal ArticleDOI
Proper Assisted Research Method Solving of the Robots Inverse Kinematics Problem
TL;DR: In this article, the authors used the Cycle Coordinate Descent Method coupled with the proper Neural Network Sigmoid Bipolar Hyperbolic Tangent (CCDM-SBHTNN) to solve the inverse kinematics problem with the goal to minimize the final end-effector trajectory errors.
Journal ArticleDOI
Optimization of the Neural Network by Using the LabVIEW Instrumentation
TL;DR: One assisted method to construct simple and complex neural network and to simulate on-line them by using the proper virtual LabVIEW instrumentation and the minimization of the error function between the output and the target is shown.
Journal ArticleDOI
Assisted Research of the Neural Network with LabVIEW Instrumentation
Adrian Olaru,Serban Olaru +1 more
TL;DR: The paper open the way to the assisted choose of the optimal neural network by using the proper virtual LabVIEW instrumentation to establish some influences of the network parameters to the number of iterations till canceled the mean square error to the target.
Journal ArticleDOI
Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem
TL;DR: The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem.