A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

The experimental proton resonance data for the reaction P+48Ti have been used to calculate and evaluate the level density by employed the Gaussian Orthogonal Ensemble, GOE version of RMT, Constant Temperature, CT and Back Shifted Fermi Gas, BSFG models at certain spin-parity and at different proton energies. The results of GOE model are found in agreement with other, while the level density calculated using the BSFG Model showed less values with spin dependence more than parity, due the limitation in the parameters (level density parameter, a, Energy shift parameter, E1and spin cut off parameter, σc). Also, in the CT Model the level density results depend mainly on two parameters (T and ground state back shift energy, E0), which are app

Purpose: The present study seeks to examine various history stages in which undergone by the concept of scenarios, and development of this concept to integration with the strategic management practices:
Methodology: The current study relied on a literature review and approach in providing total picture of different stages undergone by this concept.
The main results: the scenarios did not reach maturity in their quest for integration with strategic management, and still need a great effort for the maturation of this thought in the framework of strategic management, and through it can contribute in creating important knowledge evolution.
Originality and value: providing a contemporary model linking the roots of this concept and cu

In this paper, we present new algorithm for the solution of the nonlinear high order multi-point boundary value problem with suitable multi boundary conditions. The algorithm is based on the semi-analytic technique and the solutions are calculated in the form of a rapid convergent series. It is observed that the method gives more realistic series solution that converges very rapidly in physical problems. Illustrative examples are provided to demonstrate the efficiency and simplicity of the proposed method in solving this type of multi- point boundary value problems.

The research aims to identify the requirements of banking Entrepreneurial in Saudi Arabia and Singapore, where banking Entrepreneurial is an important way to lead employees to acquire the experience and knowledge required by the banking environment, so we note the pursuit of the banking management to acquire new technology proactively and distinctively to compete with others through the introduction of modern technologies that help senior management to develop new banking methods adaptable to the surrounding environmental changes. The problem of research highlights the extent to which the requirements of banking Entrepreneurial are applied in Saudi Arabia and the Republic of Singapore and will be addressed through three investigation

This study Ajert to modify the chemical composition of milk fat cows and make it similar to the installation of milk fat mother through the addition of protein and soybean oil to be given Alkhltatnsp sensory protein that the best plan is the ratio of 1:1

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia