Dynamics in a ratio-dependent Lotka–Volterra competitive-competitive-cooperative system with feedback controls and delays

Muhammadhaji, Ahmadjan; Halik, Azhar; Li, Hong-Li

doi:10.1186/s13662-021-03364-2

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Figure 6 describes the flowchart of the DMOA algorithm: we initialize the parameters such as dimensions, epochs, number of iterations, etc., and we also initialize the two populations of plants and mycorrhizae; with these populations we find the best fitness of plants and mycorrhizae, while with these results we use the biological operators. The first operator is represented by the Lotka-Volterra System of Discrete Equations (LVSDE) Cooperative Model [ 62 ], whose result has inference on the other two models represented by LVSDE, Defense and Competitive [ 63 , 64 ], and in this frequency we evaluate the fitness to determine if it is better than the previous one and we update the same as the populations, if not we continue with the next iteration and continue the calculation with the biological operators. If the stop condition is fulfilled we obtain the last solution before evaluation and the algorithm ends.…”

Section: Discrete Mycorrhiza Optimization Algorithmmentioning

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: Discrete Mycorrhiza Optimization Algorithmmentioning

confidence: 99%