Fuzzy-logic control as an industrial control language for embedded controllers
Design and implementation of intelligent manufacturing systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A First Course in Fuzzy and Neural Control
A First Course in Fuzzy and Neural Control
Four wheel steering control by fuzzy approach
Journal of Intelligent and Robotic Systems
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
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The paper includes reverse modeling of a diesel engine performance and emission characteristics. Modeling is done by fuzzy clustering method (FCM) and Adaptive Neural Fuzzy Inference System (ANFIS). Firstly, outputs and inputs parameters of a diesel engine were replaced as part of system. Later, these parameters were grouped into optimal numbers independently by using FCM and K-means clustering algorithm. Later on, these optimal numbers of clustered parameters were used as inputs and outputs of ANFIS to model engine performance and emissions characteristic. Input of the systems were power, torque, specific fuel consumption (sfc), nox, co2 and hc whereas outputs were air flow ratio, fuel rate, pboost, load and cycle. It has been seen that the best results obtained from ANFIS system by using FCM. What the proposed system makes different from pioneers are to be first study of reverse modeling and finding results as intervals instead of points. One more thing is that the load factor has never been implemented in previous studies but included in this study. Last but not least, the proposed system finds outputs in correct optimal interval as 100% ratio by FCM clustering and ANFIS.