Noise evaluation of MF285 and U650 tractors by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method

Abstract

In this research ANFIS method has been used to predict sound pressure levels of MF285 and U650 tractors for following machines: moldboard plow, chisel plow, cultivator, rotary tiller, boom-type sprayer, disk harrow and ditcher. Combination of fuzzy logic with architectural design of neural network leads to creation of neuro-fuzzy systems, which benefit from feed forward calculation of output and back-propagation learning capability of neural networks, while keeping interpret-ability of a fuzzy system. An adaptive neuro-fuzzy inference system architecture based on the Takagi-Sugeno model created to modeling of sound pressure level of MF285 and U650 tractors during agricultural operations. The testing performance of the proposed ANFIS model revealed a good predictive capacity to yield acceptable error measures with, R2= 0.917 and also RMSE= 1.06, SSE= 76.11 and MAE= 0.7495. The study recommends that the ANFIS technique can be successfully used in estimation of sound pressure level of MF285 and U650 tractors