Penggunaan penggugusan subtraktif bagi menjana peraturan kabur - Agus Priyono,; Muhammad Ridwan,; Ahmad Jais Alias,; Riza Atiq O. K. Rahmat,; Azmi Hassan,; Mohd. Alauddin Mohd. Ali, (2005)
Many methodologies to develop fuzzy logic rules have been previously studied. Afuzzy logic is well known because of its ability to offer a moderate method to translate the fuzzy, noise, unaccurate or lost input. The fuzzy logic is based on the emphirical method depending on the operator Experience comparing his understanding towards the system. According to the operation rule-based, fuzzy logic was able to process the information input immediately and also able to generate the necessary output. However, defining the rule-based quickly becomes complex if too many input and output are chosen. Depending on the system, the assessment of each possibility input might be not necessary if this very seldom or never occur. By using the fuzzy clustering algorithm, membership function could be counted based on two possible clustering methods. First, fuzzy clustering method performed in the orthogonal axis manner; the multivariable membership can be projected to onedimensional fuzzy sets. The second method is by using antecedent multi dimension membership function similar to the fuzzy cluster performed into input area. The basic idea in this paper work is how to learn and generate the optimum rules that required controlling input without decreasing the control quality. The subtractive clustering method to generate fuzzy logic rules on Takagi-Sugeno-Kang (TSK) fuzzy system has been utilized in this study. The suggested fuzzy logic is a smart technique which is applied into urban smart-traffic. This technique combined with neural network and genetic algorithm to determine the signal timing and offset time at Bandar Baru Bangi traffic junction control system. Based on the study, it is found that the system was able to generate 8 cluster center at on 30(3x10) data value at 0.3 cluster radius and also able to generate 4 cluster center at 0.5 radius with average MSE of 0.005