A minimum-of-maximum relative error support vector machine for simultaneous reverse prediction of concrete components. (August 2016) - Gou, Jin; Fan, Zong-Wen; Wang, Cheng; Guo, Wang-Ping; Lai, Xiong-Ming; Chen, Mei-Zhen
Highlights: A variant SVM that uses PSO to minimize the maximum relative error is proposed. A novel learning strategy whereby the maximum relative error is used in the SVM. The PSO-RE-MIMO SVM for simultaneous reverse prediction of concrete components. The proposed method performs better than the other three methods with the dataset. The proposed method is suitable in multiple-input multiple-output (MIMO) scenarios. Abstract: The simultaneous reverse prediction of multiple concrete components is very difficult, but very important in practical engineering applications. Thus, this paper presents a variant support vector machine (SVM) that uses particle swarm optimization (PSO) to minimize the maximum relative error (RE) in a multiple-input multiple-output (MIMO) scenario. This PSO-RE-MIMO SVM uses a novel learning strategy whereby the maximum relative error is used as a constraint in the optimization problem. Experimental results demonstrate that the PSO-RE-MIMO-SVM performs well for reverse prediction of multiple concrete components compared with the least-squares SVM, back propagation neural network, and radial basis function neural network approaches.