TY  - JOUR
T1  - Neural Networks in Business Applications
AU - Ahmed, Mohammed Khawwam 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 13
SP  - 4491
EP  - 4500
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.4491.4500
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.4491.4500
KW  - Artificial Neural Network (ANN)
KW  -neuron
KW  -transfer functions
KW  -hidden lopper supervised training
KW  -momentum factor
KW  -training tolerance
KW  -backdrop
KW  -galion
KW  -cross-validation
KW  -jackknifing andbootstrapping
AB  - Neural networks originally inspired from neuroscience provide powerful models for statistical data
analysis. Their most major feature is their ability to “learn” dependencies based on a finite number of
observations. In the context of neural networks the term “learning” means that the knowledge acquired from
the samples can be generalized to as yet, sense observation. In this sense, a neural network is often called a
learning machine. As such, neural networks might be considered as a symbol for an agent who learns
dependencies of his environment and thus, infers strategies of behavior based on al limited number of
observations. In this contribution, however, the researcher does not want to abstract from the biological origins
of neural network technique but present it as a purely mathematical model and also its statistical applications.
ER  -