Improvement of Neural Networks Artificial Output

The purpose of this research is to discuss the improvement Artificial Neural Network (ANN) outputs by increase the number of nodes in the neural network layer inputs of quantitative and qualitative terms to reach the desired results. The stage of determining the inputs of the network is the most important stage in the neural network to enable the compatibility of the model to reach of the desired results, activate the property of learning, and self-adaptation with any model owned by artificial neural networks. In some times, the neural network is not able to reach the desired goal not because of the lack of methodology of artificial neural networks, but because the input is not properly defined. Therefore, the neural network cannot identify the correct model. Simulation was adopted as an experimental method of research. As well as, the practical side was used to demonstrate the quality and efficiency of neural networks. The empirical and practical results were proving that the systematic increase in the number of nodes of the artificial neural network (ANN) inputs. leads to the improvement of the neural networks outputs and the obtain of outputs corresponding to inputs. Then, reach to the desired results.