الفهرس Only 14 pages are availabe for public view
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Abstract
Numerical simulation by computers of the dynamic evolution of complex systems and components is a fundamental phase of any modem engineering design activity: This is of particular importance for risk-based design projects which require that the system behavior be analyzed under several and often extreme conditions The traditional methods of simulation typically entail long. iterative. processes which lead to large simulation times. Often exceeding the transients real time Artiticial neural networks (ANN) may be exploited in this context. their advantages residing mainly in the speed of computation. in the capability of generalizing from few examples. in the robustness to noisy and partially incomplete data and in the capability of performing empirical input-output mapping without complete knowledge of the underlying physics Artificial neural. networks are called neural networks cause they are loosely modeled on the networks of neurons-nerve cells- that make up brains Neural networks are characterized by their ability to learn and can be described as trainable pattern recognizers The study and use of neural networks is sometimes called neurocomputing Brains perform remarkable computational feats recognizing music from just a few second of recording. or faces seen only once before - accomplishments that defeat even the most modern computers Yet brain5 stumble with arithmetic and make errors with simple logic. The reason for these anomalies might be found in the differences between brain and computer architecture. their internal structure and operating mechanisms Conventional computers possess distinct processing and memory units. controlled by programs Animal nervous systems and neural networks are instead made’ up of highly interconnected webs of simple processing units They have no specific memory locations instead information is being stored as patterns of interconnections between the processing units. Neural networks are not programmed, but are trained by examples They can therefore learn things that can not easily be stated in programs making them invaluable in a wide range of applications Neural networks are of interest to computer technologists because they ha\e the potential to offer solutions to a range of problems that have proved difficult to sake using conventional computing approaches These problems include pattern recognition. machine learning, time-series forecasting machine vision and robot control Underpinning all these is their ability to learn