Evolutionary learning of fuzzy neural network using a modified genetic algorithm
Design and application of hybrid intelligent systems
A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modeling
International Journal of Hybrid Intelligent Systems
A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts
Expert Systems with Applications: An International Journal
Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
Evolutionary Optimization of Union-Based Rule-Antecedent Fuzzy Neural Networks and Its Applications
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
International Journal of Approximate Reasoning
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
An integration of fuzzy inference systems and Genetic Algorithms for Wireless Sensor Networks
International Journal of Hybrid Intelligent Systems
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In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design,