MVC-Architecture Based Fuzzy-Neural-Networks Cloud-Computing

• Main Authors: Hsin-Wei Lee, 李芯瑋
• Other Authors: Gin-Der Wu
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/43793583221700537836

碩士 === 國立暨南國際大學 === 電機工程學系 === 102 === 　　Fuzzy Neural Network (FNN) is the most popular artificial intelligence research and is widely used in speech recognition, image processing, intelligent robotics, machine learning and data mining, etc. FNN combines the capability of fuzzy systems and artificial neural networks. The characteristic of fuzzy systems can mimic the vague information of the human brain and still make the right judgments. The most suitable FNN structure automatically adjusts after several iterations by the self-learning ability of artificial neural network. 　　FNN has self-learning ability and fuzzy inference advantages, many researchers use fuzzy neural network to solve classification problems. Thus, a service platform-FNN online which combines fuzzy neural network and cloud computing is presented in this thesis. FNN online provides instant online training, so that users can take advantage of fuzzy neural network to solve classification problems. The FNN online is built using the model (Model), View (View) and Controller (Controller), collectively known as the MVC design pattern. The concept of MVC is simple. Its development process in the software clearly defines the roles and high maintainability of the application. In the future works, we hope to join a variety of algorithms to allow the user to select the fitness one or management of membership function to provide a more complete service.