Online fuzzy least square support vector machine sintering process kinetics modeling algorithm
A technology of fuzzy least squares and support vector machines, which is applied in the direction of instruments, adaptive control, control/regulation systems, etc., and can solve problems such as expensive equipment, difficult and practical detection methods, and increased image processing complexity
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Embodiment 1
[0032] The online fuzzy least squares support vector machine sintering process dynamics modeling algorithm includes the following steps:
[0033] Step 1: Adopt fuzzy c-means objective function For online input of k+L groups of data space vectors (x 1 ,y 1 ),(x 2 ,y 2 ),…,(x l ,y l ) for fuzzy division to get its fuzzy membership degree (u 1 ,u 2 ,...u l ), then the fuzzy division of k+L group data space vectors is (x 1 ,y 1 ,μ 1 ), (x 2 ,y 2 ,μ 2),…,(x l ,y l ,μ l );in, is the cluster center vector; μ ik Indicates that the input vector of the fuzzy model at time k belongs to the membership degree of the i-th rule; d ik =||z i -x k || is space R M Inner product norm on ; q∈[1,∞] is the weighted exponent; n c is the rule number; U is the input data space; For each sampled data is the cluster center value; T is the transpose calculation; z i For each sampled data is the cluster center value; x k is the sampling data;
[0034] The fuzzy c-means object...
Embodiment 2
[0051] The online fuzzy least squares support vector machine sintering process dynamics modeling algorithm includes the following steps.
[0052] Step 1: According to the process reaction kinetics, establish the nonlinear relationship of process input and output parameters, apply LS-SVM modeling, and establish the objective function:
[0053] m i n ω , b , ξ J ( ω , b , ξ ) = m i n w , b , e ( 1 2 ω T ω + γ ...
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