M(II) Ions using amino acid L- proline as a primary ligand and either Nicotinamide or 8- hydroxyqinoline as secondary ligand, respectively: a. The mixed ligand complexes of composition,[M(pro)2(na)2]. b. The mixed ligand complexes of composition , Na[M(pro)2(Q)]. Where proline (C5H9NO2) symbolized as pro H , Nicotinamide (C6H6N2O) symbolized as (NA) , 8- hydroxyqinoline, (C9H7NO2) symbolized as (8-HQ). The ligands and the metal chlorides were brought into reaction at room temperature (37ºc) in ethanol as solvent .The reaction required the following molar ratios [(1:2:2) metal:2NA:2pro-] and [(1:1:2) metal:Q:2pro-] with M+2 ions, where M = [Mn (II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and pd(II)]. Products were found to be solid crystall

In the present work, the phthalic acid (phthH2) and 1.10 phenonthroline (phen), and their complexes were synthesized and isolated as [M(phth)(phen)2], Mn(II), Fe(II), Co(II), Ni(II) Cu(II), Zn(II), and Cd(II) ions. These complexes were characterized by elemental analysis, melting point, conductivity, percentage metal, UV–Vis, FT-IR, and magnetic moment measurements. The molar conductance indicates that all the metal complexes in DMSO are nonelectrolytic. phthalic acid (phtha), and 1,10-Phenanthroline (phen), behaved as bidentate, coordinating to the metal ion through their two oxygen and two pyridinyl nitrogen atoms respectively, as corroborated by. Electronic spectra, FTIR, spectroscopy amusement indicated that all the metal complexes ad

Two new ligands Na2[ H3B (BDIA)].0.05H2O (L1)(BDIA = 1-Boranyl-2,3-
Dihydro-1H-Indol-3-yl)]Acetic Acid and Na3[H2B(BDIA)2].0.3H2O.0.3CH3Ph (L2)
were synthesized by reaction of NaBH4 with indole -3- acetic acid (IAA) . The
coordination properties of ligands were studied with Co(II) , Ni(II) , Cu(II) and
Pt(IV) ions. Characterization and structural aspects of the prepared compounds were
elucidated by 1HNMR, FTIR electronic spectra, magnetic susceptibility, elemental
and metal analysis, thermal analysis (TG & DTG) and conductivity measurements.
The obtained data for metal complexes suggested square planar geometry for
copper complexes, octahedral geometry for nickel and platinium complexes and
tetrahedral geom

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an