Masters theses : Engineering
Permanent URI for this collectionhttps://repository.neelain.edu.sd/handle/123456789/506
Browse
7 results
Search Results
Item Engineering Specifications of Aggregates in Khartoum State for Concrete Mix and Road Design(ALNEELAIN UNIVERSITY, 2004-07) Farida Mohamed AbdallaItem Catalytic Refor ming of Heavy Naphtha of Heavy Naphtha of khar toum Refinery(AlNEElAN UNVERSLTY, 2004) Al samani Ahmed Mahmoud SalihItem Neuro Fuzzy Concepts and» Design (Case Study)(Alneelain University, 2008-07) Amir Ahmed Mohammcdl HassanThis research presents a comparison between desired and actual trajectory used to adaptive control to robot manipulators which can be used in soldering square shape. In this thesis, the path trajectory obtained using the Adaptive Neuro Fuzzy Inference System (ANFIS) is called the actual path trajectory. ANFIS has two essential parameters; the first parameter is the number of fuzzy set, and the second parameter is the number of training. The training data of manipulator used in ANFIS is calculated by forward kinematics for two links worked in two dimensions. The desired path trajectory of manipulator is obtained by inverse kinematics method. Many comparisons can be made between desired and actual path trajectory. The execution of comparisons requires various values for both number of membership function and number of training. Finally, the suitable path trajectory selected by comparison between correlation coefficients for both desired and actual path trajectory, the difference between desired and actual path trajectory is observed. And all the data used in comparisons are computed by MATLAB and Excel. Results obtained are of good value at number of training equal 75, and number of fuzzy set equal 9Item Neuro Fuzzy Concepts and Design (Case Study)(Neelain University, 2008) Amir Ahmed Mohammed HassanAbstract This research presents a comparison between desired and actual trajectory used to adaptive control to robot manipulators which can be used in soldering square shape. In this thesis, the path trajectory obtained using the Adaptive Neuro Fuzzy Inference System (ANFIS) is called the actual path trajectory. ANFIS has two essential parameters; the first parameter is the number of fuzzy set, and the second parameter is the number of training. The training data of manipulator used in ANFIS is calculated by forward kinematics for two links worked in two dimensions. The desired path trajectory of manipulator is obtained by inverse kinematics method. Many comparisons can be made between desired and actual path trajectory. The execution of comparisons requires various values for both number of membership function and number of training. Finally, the suitable path trajectory selected by comparison between correlation coefficients for both desired and actual path trajectory, the difference between desired and actual path trajectory is observed. And all the data used in comparisons are computed by MATLAB and Excel. Results obtained are of good value at number of training equal 75, and number of fuzzy set equal 9.Item Neuro Fuzzy Concepts and Design (Case Study)(Neelain University, 2008) Amir Ahmed Mohammed HassanAbstract This research presents a comparison between desired and actual trajectory used to adaptive control to robot manipulators which can be used in soldering square shape. In this thesis, the path trajectory obtained using the Adaptive Neuro Fuzzy Inference System (ANF IS) is called the actual path trajectory. ANFIS has two essential parameters; the first parameter is the number of fuzzy set, and the second parameter is the number of training. The training data of manipulator used in ANFIS is calculated by forward kinematics for two links worked in two dimensions. The desired path trajectory of manipulator is obtained by inverse kinematics method. Many comparisons can be made between desired and actual path trajectory. The execution of comparisons requires various values for both number of membership function and number of training. Finally, the suitable path trajectory selected by comparison between coirelation coefficients for both desired and actual path trajectory, the difference between desired and actual path trajectory is observed. And all the data used in comparisons are computed by MATLAB and Excel. Results obtained are of good value at number of training equal 75, and number of fuzzy set equal 9.Item تعويض انظمة التحكم العكسية الخطية : دراسة حالة(جامعة النيلين, 2008-05) محمد حسن جبارةAbstract This thesis discusses a case study tor a compensator design tor an antenna position control system. Root locus technique was used interactively in the design process utilizing Matlab and its tools. Ditlierent types of series compensators were used and a suitable PID controller has been implemented " PID compensator is able to achieve both the transient- state and steady - state accuracy for the system, which are crucial to position control systems. The design was subjected to evaluation tests and the results were good.Item تطوير وتصميم شبكة عصبية اصطناعية للتحكم الصوتي فينظومة التحكم(جامعة النيلين, 2008) داليا محمود ادم محمودAbstract This thesis has two main goals, the first goal is development and design artificial neural network architecture for specific application which is voice control, in addition to increase network generalization when it deals with new cases, this because human sound is not constant and vary each time he utterances the same word according to his health and emotional state. To achieve this goal, number of feed forward back propagation neural networks was built, and three types of transfer function was used which are Log sigmoid, Hyperbolic tangent sigmoid and Pure Liner to test performance for each of them, the experiences explained that Log sigmoid is much suitable for this application. Also this work follow effective manner in choosing numbers of layer and hidden nodes depending on trail and error but with specific strategy. The second goal is using the designed neural network to work as commands recognizer in voice control system, this system is a pressure control system which controls on petroleum pipelines and consist of pump, tank and electronic valve. Three experiments was applied to design this system, the designing on first experience depend on using isolated commands (Open, Close) and training neural network to recognize it, and then measured performance of the recognizer. The neural network here used log sigmoid for activate layer output and contain two hidden layer, number of nodes in them are 120 and 50 nodes. This design achieve performance ratio 98.5714%. The second designing using commands from two part (Open pump ,Close pump, Open Valve , Close valve ), the neural network used here has 70 nodes and one hidden layer between input and output layer. This design achieve perfonnance ratio 96.67%.The recognizer in last designing used phonemes of command to recognize it, this for make the system more general. The neural network used here has one hidden layer contain 100 nodes. This design achieve performance ratio only 23.3%. The three experiences were designed using MATLAB program, it used also for write preprocessing program which was use to extract word features.