Second order branching process with continuous state space

2012 ◽  
Vol 82 (11) ◽  
pp. 1930-1934 ◽  
Author(s):  
Akanksha S. Kashikar ◽  
S.R. Deshmukh
Keyword(s):  
State Space ◽  
Branching Process ◽  
Second Order ◽  
Continuous State Space ◽  
Continuous State

Coupled Processor: A Second-Order Continuous-State-Space Model

1990 ◽  
Vol 4 (2) ◽  
pp. 277-298 ◽  
Author(s):  
Haruhisa Takahashi
Keyword(s):  
State Space ◽  
State Space Model ◽  
Second Order ◽  
Two Dimensional ◽  
Riemann Boundary Value Problem ◽  
Riemann Boundary ◽  
Space Model ◽  
Continuous State Space ◽  
Continuous State ◽  
Order Continuous

A second-order continuous-state-space model for two-dimensional queueing systems is developed in this article. A particular problem is treated but the results can apply to some other two-dimensional queueing problems directly. The generating function for the model is obtained by applying a Riemann boundary value problem and leads to a computationally feasible solution.

Estimation in a Continuous State Space Branching Process

1992 ◽  
Vol 34 (8) ◽  
pp. 1001-1006 ◽  
Author(s):  
Mohan Kale ◽  
S. R. Deshmukh
Keyword(s):  
State Space ◽  
Branching Process ◽  
Continuous State Space ◽  
Continuous State

scholarly journals Reinforcement learning versus swarm intelligence for autonomous multi-HAPS coordination

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ogbonnaya Anicho ◽  
Philip B. Charlesworth ◽  
Gurvinder S. Baicher ◽  
Atulya K. Nagar
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Swarm Intelligence ◽  
Performance Indicators ◽  
Convergence Rates ◽  
Tuning Parameters ◽  
Continuous State Space ◽  
Continuous State ◽  
User Coverage ◽  
Better Than

AbstractThis work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The main aim of this paper is to address the continuous state-space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dip was a key weakness, making SI a more suitable algorithm within the context of this work.

scholarly journals Instantaneous brain dynamics mapped to a continuous state space

NeuroImage ◽  
2017 ◽  
Vol 162 ◽  
pp. 344-352 ◽  
Author(s):  
Jacob C.W. Billings ◽  
Alessio Medda ◽  
Sadia Shakil ◽  
Xiaohong Shen ◽  
Amrit Kashyap ◽  
...  
Keyword(s):  
State Space ◽  
Brain Dynamics ◽  
Continuous State Space ◽  
Continuous State
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