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This paper presents an online optimization method for a fuzzy controller used in the coke-oven combustion process, focusing on maintaining optimal temperature control despite large operational changes. The method employs dynamic just-in-time learning and a radial-basis-function neural network to create an adaptive model, which is optimized using an adaptive differential evolution algorithm. Simulation results indicate that this approach effectively enhances control performance in complex industrial processe

Author

alien

Language

EN