Discrete Competitive Lotka–Volterra Model with Controllable Phase Volume

Ворошилова, А. И.; Wafubwa, Jeff

doi:10.3390/systems8020017

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…The LVSDE Equations (5)–(10), have many uses in applied science. These models were first developed in mathematical biology, after which research spread to other fields [ 67 , 68 , 69 , 70 , 71 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

et al. 2023

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Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics, piezoelectricity, motors, biosensors, 3D printing, etc.

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Section: Proposed Methodsmentioning

confidence: 99%

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

et al. 2023

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“…In model ( 1), suppose that there is one population in the ecosystem under a certain natural growth rate α. e population size N(t) will gradually change over time and reach the maximum population size N * in a specific environment eventually. e logistic model was first applied to the estimation of population, and then it was widely used in population ecology and sociology [49].…”

Section: Lotka-volterra Modelmentioning

confidence: 99%

Symbiosis Evolution of Science Communication Ecosystem Based on Social Media: A Lotka–Volterra Model‐Based Simulation

Xia

Zhou

2021

Complexity

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Social media has become an important way for science communication. Some scholars have examined how to help scientists engage with social media from operational training, policy guidance, and social media services improving. The main contribution of this study is to construct a symbiosis evolution model of science communication ecosystem (SCE) between scientists and social media platforms based on the symbiosis theory and the Lotka–Volterra model to discuss the evolution of their symbiotic patterns and population size under different symbiosis coefficients. The results indicate that (1) the size of the symbiosis coefficients determines the equilibrium outcome of the symbiosis evolution of scientists and social media platforms; (2) scientists and social media platforms can promote each other’s population size under the mutualism pattern, which can achieve sustainable science communication; (3) “1 + 1 > 2” effect can only be achieved under the symmetric mutualism pattern and the growth of scientists and social media platforms is more stable and sufficient than that of other patterns. The findings will provide additional perspectives for promoting the sustainable development of science communication based on social media.

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“…By examining the stability of these points, we can assess the resilience and effectiveness of defence strategies in countering attacks and maintaining system integrity. Third, the Lotka-Volterra model provides a framework for studying the effects of population interactions and changes (Voroshilova, et al 2020). It allows us to investigate how changes in the attacker or defender populations influence each other through a feedback loop, creating a dynamic system where the populations continually adapt and respond to each other's actions (Bomze, I.M., 1995).…”

Section: Introductionmentioning

confidence: 99%

Modelling the Dynamics of Information Warfare: An Attacker-Defender Scenario Using Lotka-Volterra Equations

Pandey

Nazarov

2023

Preprint

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The dynamics of information warfare in an attacker-defender scenario pose significant challenges in today's digital age. To address these challenges, this research proposes a model based on the modified Lotka-Volterra equations, originally developed for predator-prey interactions in ecological systems, to analyze and simulate the population dynamics of attackers and defenders in information warfare. The model captures the growth, interaction, and feedback loop between the attacker and defender populations, allowing for a comprehensive understanding of their dynamics over time. In this model, the attacker equation incorporates parameters for the attacker's growth rate and the impact of defenders on attackers. Similarly, the defender equation considers the defender's natural decline rate and the influence of attackers on defenders. By discretizing time into small intervals and employing Euler's method, the model enables real-time simulation of the attacker-defender scenario. The simulation spans a time interval of 100 units, providing insights into the population dynamics and equilibrium points of the attacker and defender populations. The research findings highlight the model's effectiveness in capturing the dynamics of information warfare. The simulation results showcase the initial growth of the attacker population, followed by a decline as defenders implement effective countermeasures. Conversely, the defender population grows and stabilizes as their defensive capabilities improve. The research contributes to the growing body of knowledge in information warfare and provides insights for the development of robust defence strategies.

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Discrete Competitive Lotka–Volterra Model with Controllable Phase Volume

Cited by 10 publications

References 32 publications

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

Symbiosis Evolution of Science Communication Ecosystem Based on Social Media: A Lotka–Volterra Model‐Based Simulation

Modelling the Dynamics of Information Warfare: An Attacker-Defender Scenario Using Lotka-Volterra Equations

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