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Articles by Khalil Yaghi
Total Records ( 2 ) for Khalil Yaghi
  Khalil Yaghi
  This study is about the simulation of complex engineering systems’ damages with auxiliary of neural networks. We start off with a brief definition of details. We will discuss the use of neural networks in determining the conclusions of the study. A detailed definition of semi-trailer, which is the machine in study, will also be given. The study then moves on towards the main body of the study which will discuss in full details of the damages done on a semi-trailer. The analysis is being done using neural networks to analyze this complex phenomenon and to study the operation analytically. The discussion is ended by a conclusion that clearly indicates advantages, limitations and possible applications.
  Khalil Yaghi
  In this research, the refusal of technical objects in mass production uses Neural Network as a model. A neural network is a collection of interconnected elements or units. However, the phrase neural network means an amazing variety of things to a remarkable diversity of researchers. For biologists it refers to a mass of gray matter or, perhaps, a biologically faithful model of some part of the brain. For psychologists and other cognitive scientists, `neural` (or `connectionist`) network denotes a virtual machine architecture that has come to be seriously considered as a model of the mind. To a theoretical computer scientist, `neural network` is likely to mean a network of threshold logic gates. But to some computer scientists, a neural network is a Markov process, evolving through time in a stochastic search for globally optimal states. And to still others, a neural network is a collection of analog devices, continuously evolving in time under the direction of certain differential equations. To a physicist, a neural network may be a dynamical system evolving in time toward attractors of various types, or it might be a low-level substrate over which large-scale average behavior can be studied in the manner of statistical mechanics. To a functional analyst, a neural network is likely to be a particular kind of function approximator. To statisticians of various sorts, neural network learning is a realization of a scheme for estimating parameters and selecting among different models using Bayesian or information-theoretic or maximum-likelihood methods.
 
 
 
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