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Designing and Implementation of Neural Network using Membership Functions of Fuzzy
Background/Objectives: An extensive literature survey has been carried out and focused on variety of application and different research area for many data mining techniques in Multinational Company (MNC). Methods/Statistical Analysis: In this research paper we find the best cluster to extract meaningful data from large and complex pattern of neural network. This work is implemented on MATLAB Tool. By applying all the functions of research methodology we found the best cluster. This research work is compared with SOM Topology and can be used in so many applications such as biometric etc. Findings: Variety of application and different research area are identified which will be helpful and marked important for many data mining techniques in Multinational Company (MNC) and large organization for decision maker for all research resources. A methodology is proposed that focuses on zoning of neural network via membership function of Fuzzy Logic. This method removes the complexity of pattern extract the best sample easily. Application/Improvements: In future, this method will work on biometric like it will identify finger prints easily. It will divide the pattern and keep them into the zones and find the best one easily.
Membership Function, Neurons, Zones.
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