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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 Experienc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
2005
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| Online Access: | http://journalarticle.ukm.my/1435/ http://journalarticle.ukm.my/1435/ |
| Summary: | 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 |
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