Research on intelligent optimization separation technology of coal and gangue base on LS-FSVM by using a binary artificial sheep algorithm

•Artificial binary sheep algorithm is introduced for coal gangue separation method.•BASA algorithm helps to optimize the parameters of fuzzy SVM classifier.•Three comparison models were created to verify the model superiority.•The separation accuracy of coal and gangue was greatly improved up to 98% and 99%. The separation of coal gangue is affected by various complicated factors. Noise, complex texture characteristics and sample isolation seriously affect the extraction of gangue. In order to improve the accuracy and stability of coal gangue separation, a coal gangue separation method based on artificial binary sheep algorithm optimized hyperplane membership fuzzy least squares support vector machine comprehensive feature classifier (BASA-LS-FSVM) was proposed. The parameters of gray mean, gray frequency and entropy, energy, contrast and correlation were extracted from the gray matrix, and the parameters of fuzzy support vector machine classifier were optimized by artificial sheep binary algorithm. The improved BASA-LS-FSVM classification model was trained under the same sample. In order to verify the superiority of the model and algorithm, three comparison models are constructed: single feature classifier model, fusion feature classifier model and comprehensive feature classifier model. The synthetic feature classifier uses gray scale, texture and fusion features as the feature classifier model of normal plane least squares vector machine optimized by BASA, and trains the model under the same training samples. The training results show that the training correlation under the monomer characteristics has a better effect on the classification of characteristic quantity, and the sorting accuracy of coal gangue is 90% and 91%, respectively. The combined characteristic parameters of training frequency and ash have better separation effect, and the separation accuracy of gangue is higher. Under the comprehensive characteristics, the separation accuracy of coal gangue can reach 98% and 99%. The results show that the sorting effect is the best under the comprehensive characteristics. Compared with other models, the fitness function value of BASAA-NP-FSVM model reached the optimal value in the 48th generation, and the separation accuracy of coal gangue reached 98% and 99%, respectively.