How many preys could coexist with a shared predator in the Lotka–Volterra system?: State transition by species deletion/introduction

2020 ◽  
Vol 53 (41) ◽  
pp. 415601
Author(s):  
Hiromi Seno ◽  
Victor P Schneider ◽  
Toshihiko Kimura
Keyword(s):  
State Transition ◽  
System State ◽  
Volterra System ◽  
Shared Predator ◽  
Species Deletion

Output-Boundary Regulation for Nonlinear Nonminimum-Phase Systems

2016 ◽  
Author(s):  
Arom Boekfah ◽  
Santosh Devasia
Keyword(s):  
State Transition ◽  
Tracking Error ◽  
System State ◽  
Full System ◽  
Nonminimum Phase ◽  
Output Tracking ◽  
Output Error ◽  
Nonminimum Phase Systems ◽  
Phase Systems ◽  
Boundary Regulation

Exact output tracking requires preview information of the desired output for nonminimum-phase systems. For situations when preview information is not available, this article proposes an output-boundary regulation (OBR) approach that maintains the output-tracking error within prescribed bounds for nonlinear nonminimum-phase systems. OBR transitions the output-tracking error to zero whenever the output error reaches a set magnitude using polynomial output trajectories for each transition. The main contribution is to show that an output-transition-based OBR (O-OBR, which uses post-actuation input to transition the system state after the output-error transition is completed) can enable OBR of more aggressive output trajectories when compared to a state-transition-based OBR (S-OBR) that transitions the full system state and therefore achieves the output transition as well. Results from an example simulation system is used to illustrate the proposed OBR approach and comparatively evaluate the S-OBR and O-OBR approaches, which show that, for the example system, the O-OBR can track 3 times faster desired output trajectory than the S-OBR approach.

A New Hazard Identification Method-State Transition Graph

2011 ◽  
Vol 48-49 ◽  
pp. 71-78 ◽  
Author(s):  
Min Hu ◽  
Fang Fang Wu ◽  
Bo Zhu ◽  
Bo Lu ◽  
Jing Lei Pu
Keyword(s):  
State Transition ◽  
Hazard Identification ◽  
New Method ◽  
System State ◽  
Tunnel Engineering ◽  
Transition Graph ◽  
State Transition Graph ◽  
Engineering Data ◽  
Stimulation Experiments ◽  
Transition Graphs

It is important and difficult to identify the Hazard before a disaster happen because disaster often happens suddenly. This paper proposes a new method – State Transition Graph, which based on visual data space reconstruction, to identify hazard. The change process of the system state movement from one state to another in a certain period is described by some state transition graphs. The system state, which is safe or hazard, could be distinguished by its state transition graphs. This paper conducted experiments on single-dimension and multi-dimension benchmark data to prove the new method is effectiveness. Especially the result of stimulation experiments, based on the Yangtze River tunnel engineering data, showed that state transition graph identifies hazard easily and has better performances than other method. The State transition graph method is worth further researching.

The Research on Chaotic Signal Tracking Algorithm for SR-UKF-PF

2011 ◽  
Vol 69 ◽  
pp. 120-125
Author(s):  
Qian Tao ◽  
Lin Hu Zhu ◽  
Bo Pan
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
Particle Filtering ◽  
State Transition ◽  
Main Idea ◽  
Chaotic Signal ◽  
Tracking Algorithm ◽  
System State ◽  
Proposal Distribution ◽  
Importance Density Function

In view of the difficult in the chaotic signal detection and track in the low signal-to-noise(SNR) environment, a modified SR-UKF-PF is developed that has much better robustness than the traditional SR-UKF and gets almost the same performance as the Particle filter. The main idea of this algorithm is to calculated by the system state transition matrix and the error covariance matrix which are gained from the SR-UKF and the sequential fusion to construct the importance density function of the particle filter. Then the importance density function can integrates the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution maximumly. To demonstrate the effectiveness of this model, simulations are carried out based on tracking algorithm for the typical chaotic time series of low dimension chaos mapping and super chaos mapping. The simulation results show that this algorithm can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improves the accuracy of state estimation, and demonstrates the superiorities of particle filtering in the low SNR.

scholarly journals Application of bionic models for situation management

2020 ◽  
Author(s):  
Olga Gerget ◽  
Nataliia Markova
Keyword(s):  
State Transition ◽  
Data Representation ◽  
Explicit Representation ◽  
Control Action ◽  
Multidimensional Data ◽  
System State ◽  
Implicit Representation ◽  
Control Actions ◽  
Python Programming ◽  
Time Information

The article discusses the concept of choosing the sequence of control actions in order to minimize the possibility of the system state transition to an adverse one. For this purpose, the bionic model based on the synthesis of information approach, neural networks and a genetic algorithm is developed. The functionality of each of the model elements and their interaction are presented in this paper. Special attention is paid to neuroevolutionary interaction. At the same time, information about control actions is encapsulated in the gene, which allowed increasing the functionality of the algorithm due to multidimensional data representation. The article describes the principle of data representation in bionic models, which differs from the existing ones by the possibility of explicit or implicit representation of the control action in the chromosome. In the explicit representation one neural network is formed, it describes the effect of any of the control actions involved in the training. An implicit view creates a set of models, each of which describes the effect of only one control action. A brief description of the software implemented in the Python programming language is provided.

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