The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

Tensile strength is a critical property of Hot Mix Asphalt (HMA) pavements and is closely related to distresses such as fatigue cracking. This study aims to evaluate methods for assessing fatigue cracking in Asphalt Concrete (AC) mixes. In order to achieve optimum density at different binder contents, the mixes were compressed using a gyratory compactor. Tensile strength was assessed using the Indirect Tensile (IDT) and Semi-Circular Bend (SCB) tests. The results showed that the tensile strength measured by the SCB test was consistently higher than that measured by the IDT test at 25 °C. In addition, the SCB test showed a stronger correlation between increasing binder content and tensile strength. For binder contents ranging from 4

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be

      In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate gradient algorithm to estimate the output. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented

These days, the world is facing a global environmental and sustainability problem due to the increasing generation of large amounts of waste through construction and demolition work, which causes a serious problem for the environment. Therefore, this research was conducted to get rid of the waste disposal problems, including old glass and concrete, which were used as recycled fine aggregates. Seven different mixtures were prepared. The first mixture was with the used sand, which is glass sand, and it was adopted as a reference mixture (ORPC), and three mixtures were prepared for each of the recycled materials (waste concrete and glass) and partially replaced by glass sand in different proportions (25, 50, and 75) %. Some