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

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

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.

The current study included measuring the percent of protein in the extract of nematode Ascaridia galli that infect chickens, it was 1.157% and equivalent to 11.570 mg /L., as well as the amino acid analysis in the nematode A.galli by using a high-performance liquid chromatography technique (HPLC), as was detect five types for amino acids in this extract Leucine, Threonin, Serine, Methionine and Valine as the amount of these amino acids in the extract was as follows 132.973, 26.994, 10.453, 2.243 and 1.888 mg /L., respectively, and other amino acids which Glutamic, Histidine and Tyrosin did not exist in the nematode A.galli.

The study seeks to identify e-governance requirements in human resources management, which are (administrative requirements, technical, human, financial, and security) which is a new style differs from the traditional management pattern, where work depends on multiple forms and methods of Tools, (e. g. computers and various electronic software), by relying on powerful information systems that help to make management decisions as quickly and less effortless and less expensive to keep up with the tremendous technological advancements in the field of information and communication technology revolution.

 The sample included 132 Person who constitute a position (Director General, assistant Director General, section managers and d

     We have presented the distribution of the exponentiated expanded power function (EEPF) with four parameters, where this distribution was created by the exponentiated expanded method created by the scientist Gupta to expand the exponential distribution by adding a new shape parameter to the cumulative function of the distribution, resulting in a new distribution, and this method is characterized by obtaining a distribution that belongs for the exponential family. We also obtained a function of survival rate and failure rate for this distribution, where some mathematical properties were derived, then we used the method of maximum likelihood (ML) and method least squares developed  (LSD)