Abstract:
Nowadays, the one of sections which is studied about is Artificial Neural Network (ANN) Models. ANN researches are related to most field like optimization, control, image processing, meaning and separating language, natural language and forecasting. The inspiration of the ANNs is the power, elasticity and sensitivity of the biological brain. ANN is the mathematical model of the nerve cells, synapse and dendrites which are the main biological components of the brain. ANN is formed from simple mathematical elements. There are two kinds of learning processes in ANN; supervised and unsupervised. In the supervised learning process, the output set necessary for each input set, and both of them form the learning set. Usually, learning is used to realize by introduced to these pairs (input/output sets) to ANN. In the learning process, firstly, the input sets are given to ANN, and the output of them are computed. Afterwards, ANN changes the weights, until the desired convergence criteria level between the computed outputs and the real outputs is proved. As a result, ANN is trained and the weights at the most suitable values. In this study, the existing cost information of the factory was provided as an input for the artificial neural network, and the network was asked to yield the amount of production as an output. The study deals with an implementation of artificial neural network to determine the amount of production using the cost information obtained from the firm.
