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We prove a strong convergence theorem by a shrinking projection method for the class of mappings.
In this section, we will introduce a shrinking projection method for the class of mappings and prove strong convergence theorem.
We introduce a modified Picard-Mann hybrid iterative algorithm with the help of our method for the class of nonexpansive mappings.
Inspired by Bauschke and Combettes [1] and Takahashi et al. [4], we present a shrinking projection method for the class of mappings.
A modified Picard-Mann hybrid iterative algorithm is introduced with the help of our method for the class of nonexpansive mappings.
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Moreover, the numerical implementation of the stabilized solutions by the Tikhonov regularization method for this class of problems will be a very complex task.
The design method of observer for the class of nonlinear systems with multiple time-delays is proposed, and the gain matrix is obtained.
The method is suitable for the class of hybrid systems that concerns continuous processes driven by discrete controllers.
An independent PI/PID controller is designed for each reduced order decoupled subsystem to obtain the desired gain and phase margins, and the performance is verified on the original interactive system to show the effectiveness of the proposed design method for the general class of TITO systems.
Methods, results and discussions for the class of EKD distributions are presented in sections 3 to 8.
In the bipartite graph learning method we set parameter h to 2 in each protein class, because the cross-validation experiment provided the best prediction accuracy with h=2. Figure 4 shows the ROC curves of the bipartite graph learning method for the four classes of drug target interactions.
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