الفهرس 
Egypt’s Second Nuclear Research Reactor (ETRR-2)Only 14 pages are availabe for public view
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Abstract
The Nuclear Research Reactors plants are expected to be operated with high levels of reliability, availability and safety. In order to achieve and maintain system stability and assure satisfactory and safe operation, there is increasing demand for automated systems to detect and diagnose such failures. Artificial Neural Networks (ANNs) are one of the most popular solutions because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. The genetic algorithms (GAs) which are search algorithms (optimization techniques), in recent years, have been used to find the optimum construction of a neural network for definite application, as one of the advantages of its usage. Nowadays, Field Programmable Gate Arrays (FPGAs) are being an important implementation method of neural networks due to their high performance and they can easily be made parallel. The VHDL, which stands for VHSIC (Very High Speed Integrated Circuits) Hardware Description Language, have been used to describe the design behaviorally in addition to schematic and other description languages. The description of designs in synthesizable language such as VHDL make them reusable and be implemented in upgradeable systems like the Nuclear Research Reactors plants. In this thesis, the work was carried out through three main parts.
In the first part, the Nuclear Research Reactors accident’s pattern recognition is tackled within the artificial neural network approach. Such patterns are introduced initially without noise. And, to increase the reliability of such neural network, the noise ratio up to 50% was added for training in order to ensure the recognition of these patterns if it introduced with noise.
The second part is concerned with the construction of Artificial Neural Networks (ANNs) using Genetic algorithms (GAs) for the nuclear accidents diagnosis. MATLAB ANNs toolbox and GAs toolbox are employed to optimize an ANN for this purpose. The results obtained show the efficiency of using genetic algorithm, which can construct the high performance neural network structure for recognizing the Nuclear Research Reactors accidents patterns.
The third part is concerned with the hardware implementation of an artificial neural network that had obtained from Genetic Algorithm (GA) using MATLAB’s toolbox. The excellent hardware performance has been performed through the use of field programmable gate array (FPGA), on Xilinx chip, to diagnosis the Multi-Purpose Research Reactor of Egypt (MPR) accidents patterns, to avoid the risk of occurrence of a nuclear accident. The artificial neural network hardware model has been designed using Xilinx Software environment. Hardware implementation results presented unfold the promise of the hardware implementation of artificial neural networks for improving the operating performance of the Nuclear Research Reactors.