This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to...This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.展开更多
Polycrystalline YBa2Cu3O7-y （YBCO） and Y0.6Gd0.4Ba2-xNbxCu3O7-y （YGBNCO） compounds with 0≤x≤0.225 were synthesized using standard solid state reaction technique. The structure for all samples was characterized b...Polycrystalline YBa2Cu3O7-y （YBCO） and Y0.6Gd0.4Ba2-xNbxCu3O7-y （YGBNCO） compounds with 0≤x≤0.225 were synthesized using standard solid state reaction technique. The structure for all samples was characterized by X-ray difference （XRD） and scanning electron microscopy （SEM）. The transport properties were measured by the （FPP） method in the temperature range from 70 to 130 K. As the Nb content in the samples increased, a diffused phase indicating a niobium perovskite phase and a small amount of unidentified phase appeared. With the increase of Nb content, the superconducting transition temperature Tconset increased slowly with x≤0.125, and then it remained unchanged or slowly decreased with 0.125≤x≤0.225. It could be found that there was a slow decrease of zero-resistance temperature, Tcoffset, with the increase of Nb content. The larger transition width might result from the YBa2NbO6 phase, impurity and unidentified phases of the sample due to the Nb doping.展开更多
基金Project supported by the Grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Center for Healthcare Technology Development)
文摘This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.
基金Project supported by the Council of the Scientific Research Projects at Konya University
文摘Polycrystalline YBa2Cu3O7-y （YBCO） and Y0.6Gd0.4Ba2-xNbxCu3O7-y （YGBNCO） compounds with 0≤x≤0.225 were synthesized using standard solid state reaction technique. The structure for all samples was characterized by X-ray difference （XRD） and scanning electron microscopy （SEM）. The transport properties were measured by the （FPP） method in the temperature range from 70 to 130 K. As the Nb content in the samples increased, a diffused phase indicating a niobium perovskite phase and a small amount of unidentified phase appeared. With the increase of Nb content, the superconducting transition temperature Tconset increased slowly with x≤0.125, and then it remained unchanged or slowly decreased with 0.125≤x≤0.225. It could be found that there was a slow decrease of zero-resistance temperature, Tcoffset, with the increase of Nb content. The larger transition width might result from the YBa2NbO6 phase, impurity and unidentified phases of the sample due to the Nb doping.
