藉助自适应支持向量机为延迟焦化反应过程建模

MODELING DELAYED COKING PROCESS BY ADAPTIVE SUPPORT VECTOR MACHINE

The performance of support vector regression estimation was studied. It is found that the insensitive factor ε, penalty factor, and the kernel function along with its parameter are the main factors affecting the performance of support vector regression estimation.It remains a critical unsolved problem to determine the parmaeters of SVM. Cross-validation methods are commonly used in practice to decide the parameters of SVM, but they are usually expensive in computing time. A novel adaptive support vector machine (A-SVM) was proposed to determine the optimal parameters adaptively. The algorithms for adaptively tuning parameters of SVM were worked out. A-SVM was successfully applied in modeling delayed coking process. Compared with RBFN-PLSR methods, A-SVM was superior in both fitting accuracy and prediction performance. The proposed algorithms in general may be used in modeling complex chemical processes.

The performance of support vector regression estimation was studied. It is found that the insensitive factor Ε, penalty factor, and the kernel function along with its parameter are the main factors affecting the performance of support vector regression estimation. It remains a critical unsolved problem to determine the parameters of SVM. Cross-validation methods are commonly used in practice to decide the parameters of SVM, but they are usually expensive in computing time. A novel adaptive support vector machine (A-SVM) was proposed to determine the optimal parameters adaptively. The algorithms for adaptively tuning parameters of SVM were worked out. A-SVM was successfully applied in modeling delayed coking process. Compared with RBFN-PLSR methods, A-SVM was superior in both fitting accuracy and prediction performance. The proposed algorithms in general may be used in modeling complex chemical processes.