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This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise.(More)
—This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero-or first-order Takagi–Sugeno–Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies,(More)
This paper proposes a multi-objective, rule-coded, advanced, continuous-ant-colony optimization (MO-RACACO) algorithm for fuzzy controller (FC) design and its application to multi-objective, wall-following control for a mobile robot. In the MO-RACACO-based FC design approach, the number of rules and all free parameters in each rule are optimized using the(More)
This paper will apply product capability analysis chart PCAC in Measure step of Six Sigma. Because the test model is sampling, sampling error must be consider. Thus minimum value will be used to evaluate process capability. Besides the paper used minimum value to evaluate process capability, we also applied the concept of Six Sigma in PCAC and construct(More)