Attracted by the advantages of support vector regression and incremental learning
approach, it is proposed in this work that an incremental support vector regression (ISVR) …

Because of its excellent small sample learning abilities and simple network structure,
support vector machine (SVM) is widely applied in various pattern recognition fields, eg, face …

In this paper, we present an application of support vector machines (SVMs) and particle
swarm optimisation (PSO) to fault diagnosis. SVMs have been successfully employed to …

In order to solve the challenging problem of diagnosis for sensor bias and drift faults, a
method of sensor fault diagnosis based on the least squares support vector machine …

As the flight envelope is widening continuously and operational capability is improving
sequentially, gas turbine engines are faced with new challenges of increased operation and …

Fault prediction raises more and more concern because it can predict the fault to refrain from
large calamity. As time pass by, system performance is frequently changed in engineering …

In view of the bad diagnosing capability of standard support vector classifier machine (SVC)
for fault diagnosis pattern series with Gaussian noises, Gaussian function is used as loss …

Due to the poor working conditions of an engine, its control system is prone to failure. If
these faults cannot be treated in time, it will cause great loss of life and property. In order to …

Support vector machines (SVMs) are the effective machine-learning methods based on the
structural risk minimization (SRM) principle, which is an approach to minimize the upper …

Fault diagnosis is always a crucial and challenging technology in industry, which contains
huge amount of variables need to be measured and analyzed. A high-efficiency fault …