A minimum-of-maximum relative error support vector machine for simultaneous reverse prediction of concrete components

• 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.

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.