Time prediction models of grapple skidder HSM 904 using multiple linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS)

Abstract

The majority of productivity studies in forest operations use the ordinary least square regression in order to calculate time prediction models. Objective of this paper was to compare two methods, the adaptive neuro fuzzy inference system (ANFIS) and the multiple linear regression (MLR) pertaining to the modeling the loading time with the grapple skidder HSM 904. In order to investigate the influence of changes in membership functions (MF), inference system (IS) and optimization methods on the performance of the ANFIS model, four types of MF, two types of IS and two types of optimization methods were applied. Two stepwise (forward and backward) techniques were applied in the development of the MLR models. The data originated from a time study of 35 truckloads including 238 loading cycles with the specific forest machine. The comparison of the various modeling approaches indicated that the generated ANFIS with constant IS, Backpropagation training algorithm, and Gaussian membership function had a greater predictive power (R2 = 0.84) and higher performance than all calculated MLR models (R2 = 0.61). The results also showed that ANFIS could predict with a relatively high accuracy (R2 = 0.74) the loading time by adopting the “number of loaded logs” as the single input variable.