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Due to health problems on food industry, it is necessary to control exact mixing rate of some fruit juices. In this study; whole mixing systems with automation is investigated for different flow rates in the pipes. On the other hand, a robust analyzer is designed to predict real time vibrations on the system. Furthermore, from other investigations; neural networks have superior performance to predict such problems. For that reason, three types of neural networks are used to predict vibrations on different points of three tank mixing system. The results are improved that the proposed Radial Basis Neural Network (RBNN) has good performance at adapting vibration problems on mixing system. Finally, this type of neural network will be employed to analyze food industries automation systems.