particle swam optimization (pso) algorithm (Human Kinetics Inc)
90
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Human Kinetics Inc
particle swam optimization (pso) algorithm
Particle Swam Optimization (Pso) Algorithm, supplied by Human Kinetics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/particle+swam+optimization+(pso)+algorithm/particle+swam+optimization++pso++algorithm/pm29651908-35-272-268
Average 90 stars, based on 1 article reviews
Particle Swam Optimization (Pso) Algorithm, supplied by Human Kinetics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/particle+swam+optimization+(pso)+algorithm/particle+swam+optimization++pso++algorithm/pm29651908-35-272-268
Average 90 stars, based on 1 article reviews
particle swam optimization (pso) algorithm - by Bioz Stars,
2026-10
90/100 stars
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Transformation Assay:Article Title: Design of Eccentric Training System Based on Multiple-Input Single-Output Wiener Nonlinear Model. Article Snippet: According to9 the polynomial nonlinearity was introduced as the nonlinear part of the Wiener system, and Eq. (8) was transformed into: y(k) = ∑ Gi(p, z)ui(k) − c2y 2(k) − c3y 3(k) − ⋯ − cncy nc(k)ri=1 (9) Define the information vectors and the parameter vectors φs(k) ≔ [u1(k), u1(k − 1), ⋯ , u1(k − p + 1), u2(k), u2(k − 1), ⋯ , u2(k − p + 1), ur(k), ur(k − 1), ⋯ , ur(k − p + 1)] T ∈ Rrp φs(k) ≔ [ u1(k), u1(k − 1), ⋯ , u1(k − p + 1), u2(k), u2(k − 1), ⋯ , u2(k − p + 1), ur(k), ur(k − 1), ⋯ , ur(k − p + 1) ] T ∈ Rrp φc(k) ≔ [−y 2(k), −y3(k), ⋯ , −ync(k)]T ∈ Rnc−1 φ(k) ≔ [ φs(k) φc(k) ] ∈ Rrp+nc−1 θs ≔ [a11, a12, ⋯ , a1p,a21, a22, ⋯ , a2p,ar1, ar2, ⋯ , arp,] T ∈ Rrp θc ≔ [c2, c3, ⋯ , cnc] T ∈ Rnc−1 θ≔ [ θs θc ] ∈ Rrp+nc−1 From Eq. (9), the following identification model was obtained: y(k) = φs T(k)θs + φc T(k)θc = φ T (k)θ (10) For )1(PartP , the model was: y(k) = a11u1(k) + a12u1(k − 1) + c2y 2(k) + c3y 3(k) (11) D ow nl oa de d by o n 05 /2 9/ 18 , V ol um e ${ ar tic le .is su e. vo lu m e} , A rt ic le N um be r ${ ar tic le .is su e. is Plasmid Preparation:Article Title: Design of Eccentric Training System Based on Multiple-Input Single-Output Wiener Nonlinear Model. Article Snippet: According to9 the polynomial nonlinearity was introduced as the nonlinear part of the Wiener system, and Eq. (8) was transformed into: y(k) = ∑ Gi(p, z)ui(k) − c2y 2(k) − c3y 3(k) − ⋯ − cncy nc(k)ri=1 (9) Define the information vectors and the parameter vectors φs(k) ≔ [u1(k), u1(k − 1), ⋯ , u1(k − p + 1), u2(k), u2(k − 1), ⋯ , u2(k − p + 1), ur(k), ur(k − 1), ⋯ , ur(k − p + 1)] T ∈ Rrp φs(k) ≔ [ u1(k), u1(k − 1), ⋯ , u1(k − p + 1), u2(k), u2(k − 1), ⋯ , u2(k − p + 1), ur(k), ur(k − 1), ⋯ , ur(k − p + 1) ] T ∈ Rrp φc(k) ≔ [−y 2(k), −y3(k), ⋯ , −ync(k)]T ∈ Rnc−1 φ(k) ≔ [ φs(k) φc(k) ] ∈ Rrp+nc−1 θs ≔ [a11, a12, ⋯ , a1p,a21, a22, ⋯ , a2p,ar1, ar2, ⋯ , arp,] T ∈ Rrp θc ≔ [c2, c3, ⋯ , cnc] T ∈ Rnc−1 θ≔ [ θs θc ] ∈ Rrp+nc−1 From Eq. (9), the following identification model was obtained: y(k) = φs T(k)θs + φc T(k)θc = φ T (k)θ (10) For )1(PartP , the model was: y(k) = a11u1(k) + a12u1(k − 1) + c2y 2(k) + c3y 3(k) (11) D ow nl oa de d by o n 05 /2 9/ 18 , V ol um e ${ ar tic le .is su e. vo lu m e} , A rt ic le N um be r ${ ar tic le .is su e. is |