The nature of statistical learning theory
The nature of statistical learning theory
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Ant colony optimization theory: a survey
Theoretical Computer Science
Chaotic sequences to improve the performance of evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On chaotic simulated annealing
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Fault diagnosis is a small sample problem as fault data are absent in the real production process. To tackle it, Support Vector Machines (SVM) is adopted to diagnose the chemical process steady faults in this paper. Considering the high data dimensionality in the large-scaled chemical industry seriously spoil classification capability of SVM, a modified adaptive chaotic binary ant system (ACBAS) is proposed and combined with SVM for fault feature selection to remove the irrelevant variables and ensure SVM classifying correctly. Simulation results and comparisons of Tennessee Eastman Process show the developed ACBAS can find the essential fault feature variables effectively and exactly, and the SVM fault diagnosis method combined with ACBAS-based feature selection greatly improve the diagnosing performance as unnecessary variables are eliminated properly.