Patent ID: 11875488
Assignee: HENAN UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 3:
4. The method of claim 3, wherein 4) is implemented as follows:
4.1) establishing a decider training dataset {Sek, Esek}k=1Ne, where Sek is an output vector from individual deciders; sek=wrsrek+wvsvek, wr and wv respectively denote weights for real and virtual ends, srek and svek respectively denote a class number or a probability of belonging to a certain class for the real and virtual ends of the individual decider, k=1, . . . , Ne, Ne denotes a number of samples in the decider training dataset, Sek=(s1ek, . . . , sDek), siek denotes an output from the ith parallel multi-layer decomposed interval type-2 intuitionistic fuzzy convolutional neural network, i=1, . . . , D, and Esek denotes an output label;
4.2) training, by the established decider training dataset, an intuitionistic fuzzy decider based on hybrid parameter optimization:, Ys
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where α∈(0,1), Ysek denotes a final output from the decider, and siek denotes an output from the ith parallel multi-layer decomposed interval type-2 intuitionistic fuzzy convolutional neural network, i=1, . . . , D; wj and vj denote intuitionistic fuzzy rule consequents;  and  denote a triangular norm and triangular conorm; j=1, 2, . . . , M, M denotes the number of fuzzy rules; i=1D and i=1D denote  and  operations from 1 to D;
adjusting the precondition parameters of the intuitionistic fuzzy decider by a batch gradient descent method, and estimating the consequent parameters of the intuitionistic fuzzy decider by a least square method, the whole process comprising:
4.2.1) randomly initializing a center cij, a width σij and a scale coefficient rij for the precondition membership function and non-membership function of the intuitionistic fuzzy decider, and adjusting the precondition parameters by the batch gradient descent method:

∇E(cij,σij,rij,α)=(∂E/∂cij,∂E/∂σij,∂E/∂rij,∂E/∂α)  (21)

where ∇E(cij, σij, rij, α) denotes a gradient vector, E=½Σk=1Nb(Ysek−Esek)2, which is a loss function, and Nb denotes the number of data in each batch;
4.2.2) calculating a firing strength for a fuzzy rule membership function and a fuzzy rule non-membership function by using the precondition parameters adjusted in the S4.2.1, i.e., fj(Sek) and gj(Sek), and normalizing to obtain f′j(Sek)=fj(Sek)/Σj=1Mfj(Sek) and g′j(Sek)=gj(Sek)/Σj=1Mgj(Sek); for a batch of data, obtaining matrices F′ and G′:, F
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according to Formula (22), obtaining a matrix synthesized by F′ and G′: Φ=[αF′|(1−α)G′]; for some consequent parameters of the fuzzy rule membership function W=[w1 . . . wM]T and the other consequent parameters of the fuzzy rule non-membership function V=[v1 . . . vM]T, obtaining a parameter vector Θ=[WT VT]T, where ΦΘ=ESe according to Formula (20) because Φ is a Nb×2M matrix and ESe is a Nb×1 output label vector, and obtaining a consequent parameter vector Θ=Φ+ESe by generalized inverse.