Neuro-fuzzy modelling of production process

Application of classic methods based on mathematical models for production control in conditions of uncertainty doesn't allow as to create adequate algorithms that provide an effective control. Therefore, the idea of developing production system intelligent control methods in conditions of uncertainty based on simulation is an actual one. Industrial quality control is an area which gained growing momentum within the last years due to the increasing complexity and quality demands. This production system has three general purposes to achieve: first, to provide a technical-economic solution of production planning, secondly to offer a solution for operation management tasks regarding production and safe processes considering market situation, and thirdly to present a solution corresponding to the predicted parameters of the system state, taking into account the uncertainty of the environment action.