In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showed the effectiveness of the proposed control algorithm in terms of fast and smooth dynamic response for the speed control of the real DC motor.
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This research aims at examining the expected gap between the fact of planning and controlling process of production at the State Company for Electric Industries and implementation of material requirements planning system in fuzzy environment. Developing solutions to bridge the gap is required to provide specific mechanisms subject to the logic of fuzzy rules that will keep pace with demand for increased accuracy and reduced waiting times depending on demand forecast, investment in inventory to reduce costs to a minimum.
The proposed solutions for overcoming the research problem has required some questions reflecting the problem with its multiple dimensions, which ar
... Show MoreThis research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-f
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In the present study, composites were prepared by Hand lay-up molding. The composites constituents were epoxy resin as a matrix, 6% volume fractions of glass fibers (G.F) as reinforcement and 3%, 6% volume fractions of preparation natural material (Rice Husk Ash, Carrot Powder, and Sawdust) as filler. Studied the erosion wear behavior and coating by natural wastes (Rice Husk Ash) with epoxy resin after erosion. The results showed the non – reinforced epoxy have lower resistance erosion than natural based material composites and the specimen (Epoxy+6%glass fiber+6%RHA) has higher resistance erosion than composites reinforced with carrot powder and sawdust at 30cm , angle 60
... Show MoreThe research aims to study the effect of an increase in funding the Equity by issuing new common shares on revenues ordinary shares, despite the issuance and marketing costs and the introduction of new shareholders that companies incur when issuing new common shares but it is the most important methods used to finance the Equity is funding the common shares it provides money sufficient to finance the large investments of the company and enhance the confidence of dealers with the company, so I designed this research in order to identify the impact of increased funding Equity issue new common shares to common shares revenues.
This research has included some of the theoretical concepts to each of the Equity
... Show MoreObjective: The study aimed to identify the adolescents' family meal eating patterns, and find out the relationship between adolescents' family meal eating patterns and their weight control behaviors. Methodology: A descriptive study was conducted on impact of adolescents' family meal eating patterns upon their weight control behaviors in secondary schools at Baghdad city, starting from 20th of April 2013 to the end of October 2014. Non- probability (purposive) sample of 1254 adolescents were chosen from secondary schools of both sides of Al-Karkh and Al-Russafa sectors. Data was collected through a specially
The choice of binary Pseudonoise (PN) sequences with specific properties, having long period high complexity, randomness, minimum cross and auto- correlation which are essential for some communication systems. In this research a nonlinear PN generator is introduced . It consists of a combination of basic components like Linear Feedback Shift Register (LFSR), ?-element which is a type of RxR crossbar switches. The period and complexity of a sequence which are generated by the proposed generator are computed and the randomness properties of these sequences are measured by well-known randomness tests.
In the midst of the public narrative film , we discussed the issue of dealing with the current narrative more specialized which its future dimension form shift and twist on the level of construction narrative film in some of its diverse and renewable trends in its two ways theoretical and practical and non-give important implications for the modes of operation of the renewable narrative structures and the fact that the cinema is an arts which always open to ideas in neighboring fields and this come naturally as a result of the continuing intellectual and knowledge sharing between cinema and other science . our current research has already begun to address the issue in a stream of consciousness narrative structure film based on all that t
... Show MoreCredit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering res
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