@article{MAKHILLJEAS200721212920,
    title = {Modeling of Technical Objects’ Refusal with the Help of Neural Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {2},
    number = {12},
    pages = {1791-1794},
    year = {2007},
    issn = {1816-949x},
    doi = {jeasci.2007.1791.1794},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2007.1791.1794},
    author = {Khalil Yaghi},
    keywords = {Neural Networks (NN),refusal of Technical Objects (TO),modeling of technical objects,dynamical system},
    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.}
    }