Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A new fuzzy controller for stabilization of parallel-type double inverted pendulum system
Fuzzy Sets and Systems
Anti-swing and positioning control of overhead traveling crane
Information Sciences: an International Journal
On the Monotonicity of Single Input Type Fuzzy Reasoning Methods
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Realization of XOR by SIRMs Connected Fuzzy Inference Method
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Control of the TORA system using SIRMs based type-2 fuzzy logic
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
On the equivalence of single input type fuzzy inference methods
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Nonlinear function approximation using fuzzy functional SIRMs inference model
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Prediction of nasopharyngeal carcinoma recurrence by neuro-fuzzy techniques
Fuzzy Sets and Systems
SIRMs connected fuzzy inference method adopting emphasis and suppression
Fuzzy Sets and Systems
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Single input rule modules (SIRMs) dynamically connected fuzzy inference model is proposed for plural input fuzzy control. For each input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy inference model. The proposed model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart.