A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process

In the municipal solid waste incineration (MSWI) process, it is critical to predict furnace temperature, which is closely related to the incinerate state and the steam production, to maintain the high efficiency in the incineration process. In this paper, a novel self-organizing TS fuzzy neural network with an improved gradient descent algorithm (SOTSFNN-IGA) is developed to predict furnace temperature. Firstly, to get a suitable network structure and achieve high-efficiency computing capability, the error criteria and activity intensity are employed to grow and remove the fuzzy rules of SOTSFNN-IGA automatically. Secondly, an improved gradient descent algorithm is employed to adjust the parameters of SOTSFNN-IGA. Thirdly, the convergence analysis of the proposed SOTSFNN-IGA is given through the Lyapunov theory. Subsequently, to understand the influence of each variable on the furnace temperature, a new variable importance measurement method is employed. Finally, the proposed SOTSFNN-IGA is verified based on several benchmark nonlinear systems and a furnace prediction in the MSWI process. Experimental results demonstrate that the developed SOTSFNN-IGA has better advantages in prediction accuracy than other algorithms, which prediction accuracy and NSE coefficient are as high as 99.85% and 0.9827 respectively in the furnace temperature prediction.