Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller

As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.

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