文件名称:66357
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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.
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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下载文件列表
TS_Function.pdf
ts.m
tsml.m
license.txt
ts.m
tsml.m
license.txt
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