Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
A self-organizing neural-network-based fuzzy system
Fuzzy Sets and Systems
Application of statistical information criteria for optimal fuzzy model construction
IEEE Transactions on Fuzzy Systems
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Fuzzy Modeling Based on Ordinary Fuzzy Partitions and Nearest Neighbor Clustering
Journal of Intelligent and Robotic Systems
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
A new T-S fuzzy-modeling approach to identify a boiler-turbine system
Expert Systems with Applications: An International Journal
Modeling ph neutralization process via support vector machines
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Adaptive control scheme based on the least squares support vector machine network
International Journal of Applied Mathematics and Computer Science
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Fuzzy models have been proved to have the ability of modeling all plants without any priori information. However, the performance of conventional fuzzy models can be very poor in the case of insufficient training data due to their poor extrapolation capacity. In order to overcome this problem, a hybrid grey-box fuzzy modeling approach is proposed in this paper to combine expert experience, local linear models and historical data into a uniform framework. It consists of two layers. The expert fuzzy model constructed from linguistic information, the local linear model and the T-S type fuzzy model constructed from data are all put in the first layer. Layer 2 is a fuzzy decision module that is used to decide which model in the first layer should be employed to make the final prediction. The output of the second layer is the output of the hybrid fuzzy model. With the help of the linguistic information, the poor extrapolation capacity problem caused by sparse training data for conventional fuzzy models can be overcome. Simulation result for pH neutralization process demonstrates its modeling ability over the linear models, the expert fuzzy model and the conventional fuzzy model.