In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

   Deficiencies in revenue-related accounting standards, including American accounting standards as well as international accounting standards, prompted the issuance of the International Financial Reporting Standard IFRS 15 "Revenue from contracts with customers" as part of the convergence plan between the FASB and the International Accounting Standards Board (IASB) according to the requirements of The joint venture between the two councils, whereby the standard aims to define the basis for reporting useful information to the users of the financial statements about the nature, amount, timing and uncertainty about the revenues and cash flows arising from a contract with the customer, The standard is base

Burdock ( Arctium lappa), is among the most popular plants in traditional medicine and it is associated with several biological effects. Literature survey revealed the presence of phenylpropanoid compounds .The most widespread are hydroxycinnamic acids ( mainly caffeic acid and chlorogenic acid) and lignans (mainly arctiin and arctigenin). This work will confirm the presence of these compounds in Arctium lappa, cultivated in Iraq, in both root and leaf samples.  The dried plant samples were extracted by soxhlet with 80% methanol then separated the main constituents by thin layer chromatography (TLC) and high performance liquid  chromatography (HPLC). Identification of the isolated compounds wa

This study presents a practical method for solving fractional order delay variational problems. The fractional derivative is given in the Caputo sense. The suggested approach is based on the Laplace transform and the shifted Legendre polynomials by approximating the candidate function by the shifted Legendre series with unknown coefficients yet to be determined. The proposed method converts the fractional order delay variational problem into a set of (n ＋ 1) algebraic equations, where the solution to the resultant equation provides us the unknown coefficients of the terminated series that have been utilized to approximate the solution to the considered variational problem. Illustrative examples are given to show that the recommended appro

The Fauqi field is located about 50Km North-East Amara town in Missan providence in Iraq. Fauqi field has 1,640 MMbbl STOIIP, which lies partly in Iran. Oil is produced from both Mishrif and Asmari zones. Geologically, the Fauqi anticline straddles the Iraqi/Iranian border and is most probably segmented by several faults. There are several reasons leading to drilling horizontal wells rather than vertical wells. The most important parameter is increasing oil recovery, particularly from thin or tight reservoir permeability. The Fauqi oil field is regarded as a giant field with approximately more than 1 billion barrels of proven reserves, but it has recently experienced low production rate problems in many of its existing wells. This study

Abstract

     This research deals with Building A probabilistic Linear programming model  representing, the operation of production in the Middle Refinery Company (Dura, Semawa, Najaif) Considering the demand of each product (Gasoline, Kerosene,Gas Oil, Fuel Oil ).are random variables ,follows certain probability distribution, which are testing by using Statistical programme (Easy fit), thes distribution are found to be Cauchy distribution ,Erlang distribution ,Pareto distribution ,Normal distribution ,and General Extreme value distribution .              &