Wanditra, Lucky Cahya
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Representation Theory of Recurrent Neural Network Wanditra, Lucky Cahya; Alamsyah, Intan Muchtadi; Nasution, Dellavitha
Journal of the Indonesian Mathematical Society Vol. 31 No. 2 (2025): JUNE
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i2.1833

Abstract

In this paper, we use the representation morphism concept to analyze the connection between two recurrent neural networks, primarily when we evaluate the neural network function between two isomorphic neural networks. We construct the set of all isomorphic classes of recurrent neural networks. We build the set by the action of the isomorphism group on the set of all recurrent neural networks that have invertible weight. By the group’s action, we get the set of orbits and call it the moduli space. We analyze the moduli space to get its dimensions.
Dynamical System Modeling of Dynastic Cycle with Optimal Control Mahardika, Dhimas; Ariyani, Rizki Chika Audita; Kencono, Uvi Dwian; Wanditra, Lucky Cahya; Rahmasari, Shafira Meiria
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2692

Abstract

In ancient China there are three model of society which is farmers, bandits and ruler. When the authority (rulers) is not there, the dynamics system of farmers and bandits become predator-prey interactions system. In here rulers play role on taxing the farmers and catching the bandits and then punish them. Thus, farmers are a sort of renewable resource which is exploited both by bandits and by rulers. In this paper, optimal control is applied to reduce the bandit’s population, by reducing it, the ruler population can also be reduced because the existing bandits can be conquered, so that the cost of running a government is more efficient because it can reduce the need for eradicating bandits from ruler. The type of the optimal control here is fixed time and free end point.