The neural network system using the quality-prediction of the carbon is the most promising application of the artificial intelligence technology applied in the industrial production process.
The use of this technique makes it possible to predict the quality of carbon products accurately without the need for expensive physical laboratories.
Basically, a neural network (NN) is an artificial system that can make decisions based on the inputs it receives from the environment. In the context of carbon quality prediction, the inputs are the properties of the feed material, such as the particle size, density, physical and chemical properties, etc. The neural network model can be trained with these input data as well as corresponding outputs that indicate the quality of the product.
The neural network system configured for the quality-prediction of the carbon can be further divided into two-levels. The first level is feature selection, where the input is filtered to select the most meaningful parameters for the quality-prediction task. Then, the second level uses these selected features to train the neural network.
At the feature selection level, the input data is fed into the neural network system, which then selects the most appropriate parameters that would contribute to the prediction of the quality of the carbon product. This can be one of the most difficult tasks as it involves recognizing patterns and trends in the data before selecting appropriate parameters.
At the second stage of training the neural network, the filtered input data is used to train the neural network model. The main goal of the training is to optimize the weights of the neurons to develop an appropriate solution that accurately predicts the quality of the product. Furthermore, this solution must be remember in order to make the result applicable to a wide variety of input data.
The result of applying the neural network system in the quality-prediction of the carbon products can be clearly seen in terms of the precision and accuracy of the results obtained. In addition, this type of system is cost-effective and can be used not only to predict the quality of existing products, but also to identify the problems that could lead to an inadequate quality in the finished product.
Therefore, it can be concluded that the neural network system is a promising tool for improving the quality of carbon products. With this system, it is possible to capture the complex patterns in the production process and to accurately predict the quality of the product. This can ultimately help to reduce costs and increase the efficiency of the process.
