Registered Data

[CT057]

[01308] Disturbance estimation-based predictive tracking control design for semi-Markovian jump systems with time-varying input delay

  • Session Date & Time : 1E (Aug.21, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : A state tracking control problem for a class of semi-Markovian jump systems with uncertainties and disturbances is addressed in this study. In particular, an improved-equivalent-input-disturbance estimator approach-based truncated predictive controller is designed to synchronously compensate the effect of unknown external disturbances and time-varying delay. Specifically, the state predictor satisfies the delay-free state-space equation and is used in controller design along with prediction. Finally, the numerical examples with simulation results are provided.
  • Classification : 37N35, 93D05, 93D09, 93C05, 93D20
  • Author(s) :
    • Harshavarthini Shanmugam (Assistant Professor)

[00740] Accurate nonlinear reduction of low-dimensional data in a high-dimensional space

  • Session Date & Time : 1E (Aug.21, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : We propose an accurate method to analyze data lying on a low-dimensional manifold in an underlying space of possibly high dimension. The data is first joined by simplexes to form a network. The connected structure is deformed and relaxed to minimize a certain fictitious energy. This process yields a suitable nonlinear transformation. The resulting data is processed just by the principal component analysis. We show examples using data from several exactly integrable dynamical systems.
  • Classification : 37N99, 65P99, 70K25
  • Author(s) :
    • Shinya Watanabe (Ibaraki University)
    • Kotone Nemoto (Ibaraki University)

[01924] Mixed Leader-Follower Dynamics

  • Session Date & Time : 1E (Aug.21, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : The original Leader-Follower (LF) model partitions all agents whose opinion is a number in $[-1,1]$ to a follower group, a leader group with a positive target opinion in $[0,1]$ and a leader group with a negative target opinion in $[-1,0]$. A leader group agent has a constant degree to its target and mixes it with the average opinion of its group neighbors at each update. A follower has a constant degree to the average opinion of the opinion neighbors of each leader group and mixes it with the average opinion of its group neighbors at each update. In this paper, we consider a variant of the LF model, namely the mixed model, in which the degrees can vary over time, the opinions can be high dimensional, and the number of leader groups can be more than two. We investigate circumstances under which all agents achieve a consensus. In particular, a few leaders can dominate the whole population.
  • Classification : 37N99, 05C50, 91C20, 93D50, 94C15
  • Author(s) :
    • Hsin-Lun Li (National Sun Yat-Sen university )

[00491] Introduction and Advances in Time Series Analysis With Reservoir Computing

  • Session Date & Time : 1E (Aug.21, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : Reservoir computers have proven to be powerful embedding machines for dynamical systems. However, bridging the gap from their machine learning origins to time series analysis is still relatively new, with great potential for novel discoveries. In this talk, we will outline what reservoir time series analysis is and why one should care about it amidst the ecosystem of other embedding-based techniques. We will then present some use cases and applications to motivate future work.
  • Classification : 37Nxx, 68Uxx, 62Lxx, Time Series Analysis, Reservoir Computing, Change Point Detection
  • Author(s) :
    • Braden John Thorne (University of Western Australia)
    • Michael Small (University of Western Australia)
    • Débora Cristina Corrêa (University of Western Australia)
    • Ayham Zaitouny (University of Doha for Science and Technology)

[01443] Absolute concentration robustness in reaction networks with power-law kinetics

  • Session Date & Time : 1E (Aug.21, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : Absolute concentration robustness or ACR is a condition wherein the concentration of a species remains invariant for any positive equilibrium a kinetic system may admit. Often, results on ACR are limited to reaction networks with deficiency one. Here, we use network decomposition to detect ACR among networks with higher deficiency by considering a lower deficiency subnetwork with ACR as a local property. This smaller subnetwork serves as a building block for the larger ACR-possessing network.
  • Classification : 37Nxx, 92Cxx
  • Author(s) :
    • Noel Fortun (De La Salle University, Manila)
    • Eduardo Mendoza (Max Planck Institute of Biochemistry)