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Journal of Engineering and Applied Sciences
Year: 2007 | Volume: 2 | Issue: 12 | Page No.: 1791-1794
Modeling of Technical Objects’ Refusal with the Help of Neural Networks
Khalil Yaghi
 
Abstract: 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.
 
How to cite this article:
Khalil Yaghi , 2007. Modeling of Technical Objects’ Refusal with the Help of Neural Networks . Journal of Engineering and Applied Sciences, 2: 1791-1794.
URL: http://medwelljournals.com/abstract/?doi=jeasci.2007.1791.1794