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Description: tsk fuzzy system codes
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Size: 1687732 |
Author: dzh |
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Description: tsk fuzzy system codes
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Size: 1687552 |
Author: dzh |
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Description: 一种动态模糊神经网络源码，是标准五层结构的动态模糊神经网络，该模糊神经网络的结构基于扩展的径向基神经网络，在功能上等价于TSK模糊系统。-this is a Dynamic Fuzzy neural network.
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Size: 2048 |
Author: yanbenwei |
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Description: A TSK type fuzzy controller designed by GA-by tournament.initial mutation probability
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Size: 4096 |
Author: henry |
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Description: 本人模糊控制的课堂作业，基于Sugeno（TSK）推理的模糊控制器-I am working class of fuzzy control, based on Sugeno (TSK) inference of the fuzzy controller
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Size: 67584 |
Author: 徐立 |
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Description: 本人的模糊控制课堂作业，基于Sugeno（TSK）推理的模糊控制器-Fuzzy control of my class assignments, based on Sugeno (TSK) inference of the fuzzy controller
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Size: 67584 |
Author: 徐立 |
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Description: Fuzzy Control System Design using simulink and fuzzy toolbox for nonlinear systems
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Size: 147456 |
Author: farnaz farbod |
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Description: Development of a Self-Tuning TSK-Fuzzy Speed
Control Strategy for Switched Reluctance Motor
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Size: 1810432 |
Author: chaithu |
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Description: A doucument introducing the GA - TSK FNN algorithm.
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Size: 161792 |
Author: Bao |
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Description: A document introducing the TSK-type recurrent fuzzy network with GA.
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Size: 307200 |
Author: Bao |
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Description: Fuzzy (TSK) (size 11KB) contains Matlab source code. There are 23 files include JENKIN database.
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Size: 11264 |
Author: Bongyoun Kim |
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Description: Takagi-Sugeno fuzzy models are based on the concept of fuzzy coding of information and operating with fuzzy sets instead of numbers
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Size: 12288 |
Author: Weslly Puchalski |
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Description: MATLAB CODE FOR: Minimax Probability TSK Fuzzy System Classifier:
A More Transparent and Highly Interpretable
Classification Model
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Size: 444416 |
Author: H |
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Description: This network uses gaussian membership functions with two free parameters:
mean and variance. The total number of these parameters is M*N, where M is
the number of rules, N is the number of inputs. There is a hidden set of
parameters of tsk-functions which perform a linear convolution of fuzzy
inference outputs with a set of coefficiants. Total number of these
parameters is M*(N+1). So the total amount of adjusted parameters is
2*M*N + M.
-This network uses gaussian membership functions with two free parameters: mean and variance The total number of these parameters is M* N, where M is the number of rules, N is the number of inputs There is a hidden set of parameters of tsk..-functions which perform a linear convolution of fuzzy inference outputs with a set of coefficiants. Total number of these parameters is M* (N+1). So the total amount of adjusted parameters is 2* M* N+ M.
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Size: 207872 |
Author: 陳冠廷 |
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