Dynamic ridge polynomial neural network with Lyapunov function for time series forecasting

The ability to model the behaviour of arbitrary dynamic system is one of the most useful properties of recurrent networks. Dynamic ridge polynomial neural network (DRPNN) is a recurrent neural network used for time series forecasting. Despite the potential and capability of the DRPNN, stability problems could occur in the DRPNN due to the existence of the recurrent feedback. Therefore, in this study, a su cient condition based on an approach that uses adaptive learning rate is developed by introducing a Lyapunov function. To compare the performance of the proposed solution with the existing solution, which is derived based on the stability theorem for a feedback network, we used six time series, namely Darwin sea level pressure, monthly smoothed sunspot numbers, Lorenz, Santa Fe laser, daily Euro/Dollar exchange rate and Mackey-Glass time-delay di erential equation. Simulation results proved the stability of the proposed solution and showed an average 21.45% improvement in Root Mean Square Error (RMSE) with respect to the existing solution. Furthermore, the proposed solution is faster than the existing solution. This is due to the fact that the proposed solution solves network size restriction found in the existing solution and takes advantage of the calculated dynamic system variable to check the stability, unlike the existing solution that needs more calculation steps

The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects

In this paper, We have studied a post disaster for emergency reconstruction projects. The software used is SPSS which provide an optimized method to find the optimum number of nodes in the hidden layer using training process according to the theory of ANN. The set of the input data is processed using assumed model parameters and the results outputs are found and compared with the real output , which then adjust the assumed parameters to minimize the sum of square error, this is the algorithm that the ANN use to adjust the parameters to the best values. The data was divided into two sets the training set 20 projects, to build the model (estimating the parameters), then the rest 10 project will used for verification, which proved that the output are reliable.About the originality of this paper, it was first mentioned in the abstract, besides, it is the first time that we invent an application using both ANN’s and Java script to predict deviation in cost and time before even starting the project.Findings were clearly shown in results and discussions. The used approach in analyzing data was the standard statistics and ANN’s approach that was first time used in such studies in Iraq. About the significant points, I shortly briefed that because the focus was on ANN’s first. Then these points should be made even clearer; that there is little use of ANN and delay factors, and that our main findings are related to post-emergency construction projects (like the other paper

The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects

In this paper, We have studied a post disaster for emergency reconstruction projects. The software used is SPSS which provide an optimized method to find the optimum number of nodes in the hidden layer using training process according to the theory of ANN. The set of the input data is processed using assumed model parameters and the results outputs are found and compared with the real output , which then adjust the assumed parameters to minimize the sum of square error, this is the algorithm that the ANN use to adjust the parameters to the best values. The data was divided into two sets the training set 20 projects, to build the model (estimating the parameters), then the rest 10 project will used for verification, which proved that the output are reliable.About the originality of this paper, it was first mentioned in the abstract, besides, it is the first time that we invent an application using both ANN’s and Java script to predict deviation in cost and time before even starting the project.Findings were clearly shown in results and discussions. The used approach in analyzing data was the standard statistics and ANN’s approach that was first time used in such studies in Iraq. About the significant points, I shortly briefed that because the focus was on ANN’s first. Then these points should be made even clearer; that there is little use of ANN and delay factors, and that our main findings are related to post-emergency construction projects (like the other paper)THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Power Factor Control of PV Generators in Local Distribution Networks using Fuzzy Logic Concept

ABSTRACT: One of the most important operational requirements for any electrical power network for both distribution and transmission level is voltage control. Many studies have been carried out to improve or develop new voltage control techniques to facilitate safe connection of distributed generation. In Saudi Arabia, due to environmental, economic and development perspectives a wide integration of photovoltaic (PV) generation in distribution network is expected in the near future. This development in the network may cause voltage regulation problems due to the interaction with the existing conventional control system. In a previous paper [1] a control system has been described using a fuzzy logic control to set the on-line tap changer for the primary substation. In this paper a new control system is proposed for controlling the power factor of individual PV invertors based on observed correlation between net active and reactive power at each connection. A fuzzy logic control has been designed to alter the power factor for the remote invertors from the secondary substation to keep the feeder voltage within the permissible limits. In order to confirm the validity of the proposed method, simulations are carried out for a realistic distribution network with real data for load and solar radiation. Results showing the performance of the new control method are presented and discussed. KEYWORDS: Grid connected PV system, Dnetwotk, Power factor, Fuzzy logic, Control. I.INTRODUCTION This paper presents an approach for solving the feeder voltage regulation problem in a local manner, with the goal of fulfilling the plug-and-play feature desired by manufacturers and regulatory bodies. The plug-and-play feature will enable customers to simply connect their PV systems to the distribution feeder, and through a fuzzy logic control (FLC) the power flow from the PV system through the inverters will be controlled to help maintain the feeder voltage level within limits at minimum cost. The chapter paper by the proposed controller objectives. The proposed controller design is based on FLC and the architecture is then presented. Test results of this system are shown to prove that the proposed controller can successfully regulate the voltage of a distribution feeder with PV systems connected to it. Finally the interaction between two controllers at the same feeder has been investigated and a simulation is carried out under MATLAB /Simulink environment to evaluate the stability of the control algorithm. II. CONTROL OF THE REACTIVE POWER OF PV GENERATION USING FLC Consider a voltage source V S ∟δ connected to a utility grid V G ∟0° through a coupling impedance Z=R+jX, as shown i

Epidermal growth factor receptor mutations in nonsmall cell lung carcinoma patients in Kuwait

Context: Nonsmall cell lung carcinoma (NSCLC) is the most frequently diagnosed form of lung cancer in Kuwait. NSCLC samples from Kuwait have never been screened for epidermal growth factor receptor (EGFR) gene aberration, which is known to affect treatment options. Aims: This study investigated the feasibility of using fine-needle aspiration (FNA) material for mutational screening, and whether common EGFR mutations are present in NSCLC samples from Kuwait. Settings and Design: Eighteen NSCLC samples from five Kuwaitis and 13 non-Kuwaitis were included in this study. Materials and Methods: DNA was extracted from FNA cell blocks and screened for EGFR gene mutations using peptide nucleic acid (PNA)-clamp assay, and EGFR gene amplification using fluorescent in situ hybridization (EGFR-FISH). EGFR protein expression was assessed using immunohistochemistry. Results: Five EGFR mutations were detected in five non-Kuwaiti NSCLC patients (27.8%). EGFR gene amplification was evident in 10 samples (55.5%) by direct amplification or under the influence of chromosomal polysomy. Four samples had EGFR mutations and EGFR gene amplification, out of which only one sample had coexisting EGFR overexpression. Conclusions: Given the evidence of EGFR gene alterations occurring in NSCLC patients in Kuwait, there is a need to incorporate EGFR gene mutational screen for NSCLC patients to implement its consequent use in patient treatment