The traditional evaluating methods can not deal with the evaluation problem of innovation sources in service firms with fuzzy information, authors have constructed fuzzy support vector machine based on support vector machine and fuzzy chance constrained programming, and applied this new method to evaluating innovation sources in service firms. In basis of related literature reviewing, authors h...

This paper proposed a method to identify nonlinear systems via the fuzzy weighted least squares support machine (FW-LSSVM). At first, we describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. Because the training sample data of independent variable and dependent variable has a certain error, and we obtain the sample which has a certain fuzziness f...

This paper presents a new version of fuzzy support vector machine to forecast the nonlinear fuzzy system with multi-dimensional input variables. The input and output variables of the proposed model are describedas triangular fuzzynumbers. Thenby integrating the triangular fuzzy theory andv-support vector regression machine, the triangular fuzzy v-support vector machine (TFv-SVM) is proposed. To...

When the standard genetic algorithm is used to solve the fuzzy programming problem, poor convergence occurs. In order to overcome this defect, this paper presents a hybrid intelligent evolutionary algorithm based on nonlinear support vector machine (SVM) to solve the fuzzy programming problem. Firstly, based on the research of genetic algorithm, evolutionary strategy and genetic algorithm are c...

Support vector machines (SVMs) and fuzzy rule systems are functionally equivalent under some conditions. Therefore, the learning algorithms developed in the field of support vector machines can be used to adapt the parameters of fuzzy systems. Extracting fuzzy models from support vector machines has the inherent advantage that the model does not need to determine the number of rules in advance....

Data Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won’t work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining p...

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

integrally skinned asymmetric membranes based on nanocompositepolyethersulfone were prepared by the phase separation process using the supercritical co2 as a nonsolvent for the polymer solution. in present study, the effects of temperature and nanoparticle on selectivity performance and permeability of gases has beeninvestigated. it is shown that the presence of silica nanoparticles not only di...