Download Advances in Neural Networks – ISNN 2015: 12th International by Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao PDF

By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao

ISBN-10: 3319253921

ISBN-13: 9783319253923

ISBN-10: 331925393X

ISBN-13: 9783319253930

The quantity LNCS 9377 constitutes the refereed court cases of the twelfth foreign Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers offered have been conscientiously reviewed and chosen from ninety seven submissions. those papers conceal many issues of neural network-related examine together with clever keep watch over, neurodynamic research, memristive neurodynamics, computing device imaginative and prescient, sign processing, computer studying, and optimization.

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Additional info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings

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36–42, 2015. 1007/978-3-319-25393-0_5 A Terminal-Sliding-Mode-Based Frequency Regulation 37 Ri is adjustment deviation coefficient and Kpi is electric system gain. Tij is the synchronizing power coefficient between area i and area j, i=1,…, N and N is the number of areas. Define xi (t)=[∆Xgi (t), ∆Pgi (t), ∆fi (t), ∆Ptie,i (t), ∆Ei (t)]T. ui(t)=∆Pci(t) is control input, N ∆Pdi(t)=[∆PLi(t), ∑Tij Δf j (t ) ]T is disturbance vector. Then, the system model (1) can be j =1 j ≠i deduced and employed for the LFC design of the ith control area.

Thus, the dynamic and static performance of power control is affected by the performance of the internal current controller [6]. In This work was in part supported by the National Nature Science Foundation of China under Grants 61273137, 51209026, 51579023, and in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant L2013202, and in part by the Fundamental Research Funds for the Central Universities under Grants 3132015021, 3132014321, and in part by the China Postdoctoral Science Foundation under Grant 2015M570247.

Si,j ⎡ where (21) (22) i i i,j i,j T S K = PK,m Ai,n + PK,n Ai,m − Em,n − (Fm,n ) , i,j i,j T S UK = Pj,m Ai,n + Pj,n Ai,m − Ym,n − (Wm,n ) , i,j i,j T ν = −γPi,m − γPi,n − Zm,n − (Zm,n ) , i,j Ωm = i,j Ξm = i PK,n = −Pj,n Pj,n Ai,m ∗ −γPi,m i −PK,n ∗ −( 4 i PK,n Ai,m i j∈χ πij )γPi,m j ∈ χiK , K i,j T πi,j )(Pi,m + Pi,n ) − Gi,j m,n − (Gm,n ) . 236 , B32 = . 6 ? ⎣ ? 5 ⎦ . 2 ? 04. It can be seen from the figures that the system (1) with the controller u(k) = Ki (λ)x(k) meets the specified requirement, where Ki (λ) = Ui (λ)Si (λ)−1 .

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Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings by Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao


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