The objective of this paper is to present and make a comparative study of several inverse kinematics methods for serial manipulators, based on the Jacobian matrix. Besides the well-known Jacobian transpose and Jacobian pseudo-inverse methods, three others, borrowed from numerical analysis, are presented. Among them, two approximation methods avoid the explicit manipulability matrix inversion, w...

Arti3cial neural networks (ANNs) have recently gained attention as fast and 5exible vehicles to microwave modeling, simulation, and optimization. In this study, ANNs, based on the multilayer perceptron, were presented for accurate computation of the quasistatic parameters of asymmetric coplanar waveguides (ACPWs). Multilayer perceptron neural networks (MLPNNs) were trained with backpropagation,...

This work presents the results of the studies concerning the application of different neural network training algorithms to enhance the prediction of a radio network planning tool. Investigations are made on a hybrid model that combines the a-priori information in form of simulation results with the a-posteriori knowledge contained in measurement data. The performances of Back Propagation and L...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...

The aim of this paper is to propose a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) algorithm of its learning capable to estimate states and parameters of a highly nonlinear Continuous Stirred Tank Bioreactor (CSTR) in noisy environment. The estimated parameters and states obtained by the proposed KFRNN identifier are used to design an ind...

As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The system used in this study to evaluate the optimization of BPNN based on LM algorithm proved algorithm’s efficacy through a MATLAB simulation analysis. This paper examined application impact en...