Neuro-Evolution of Continuous-Time Dynamic Process Controllers

Neuro-Evolution of Continuous-Time Dynamic Process Controllers

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Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems.In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used.But in case of controller design of dynamic systems the ps5 price south carolina required (optimal) controller output is a-priori unknown, supervised learning cannot be used.

In such case we only can define some criterion function, which represents the required control performance of the closed-loop system.We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems.The controller is represented by an MLP-type artificial neural network.

The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm.An integral-type performance index representing control quality, which tc electronic honey pot is based on closed-loop simulation, is minimised.The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.