A novel efficient learning algorithm for self-generating fuzzy neural network with applications
In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN) is proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to gene...
محفوظ في:
المؤلفون الرئيسيون: | Liu, Fan, Er, Meng Joo |
---|---|
مؤلفون آخرون: | School of Electrical and Electronic Engineering |
التنسيق: | مقال |
اللغة: | English |
منشور في: |
2013
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://hdl.handle.net/10356/96812 http://hdl.handle.net/10220/11607 |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
المؤسسة: | Nanyang Technological University |
اللغة: | English |
مواد مشابهة
-
Study of hybrid learning algorithms for realization of self-constructing fuzzy neural networks
بواسطة: Liu, Fan.
منشور في: (2012) -
Design of self-organizing fuzzy neural networks using evolutionary algorithms
بواسطة: Fan, Lihua
منشور في: (2011) -
Biomedical diagnosis and prediction using parsimonious fuzzy neural networks
بواسطة: Chen, Yuting, وآخرون
منشور في: (2013) -
Dynamic fuzzy neural networks : principles, algorithms and applications
بواسطة: Wu, Shi Qian.
منشور في: (2008) -
Automatic generation of fuzzy neural networks via reinforcement learning with applications in path planning of mobile robots
بواسطة: Zhou, Yi
منشور في: (2008)