integrating expert consensus modeling, attribute weighting, Z-number, and the Multi-Attribute Border Approximation Area Comparison (MABAC) method. Initially, a consensus model is established using consistency theory to determine expert weights, and attribute priorities are determined through the entropy weighting method. Subsequently, the Z-number-based MABAC method ranks the alternatives, determiningthe optimal solution among them. Using an automated outdoor cleaning vehicle as an example, the proposed method is compared to other techniques. The sensitivity analysis and the comparisons show that the proposed method improves the reliability and objective of the decision-making process.

of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.