A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System
Abstract
:1. Introduction
- A computational novel AI based BSR-NNs is presented to get the numerical solutions of the MDMS.
- The performance of the AI based BSR-NNs is observed to solve three different variations of the MDMS.
- For the correctness of the AI scheme portrayed by the BSR-NNs, the comparison performances using the obtained and reference solutions have been presented.
- Twelve number of hidden neurons have been taken to solve effectively the MDMS by applying the BSR-NNs.
- The absolute error (AE) is achieved in exceptional performances that demonstrate the accuracy of the BSR-NNs.
- For the solution of the dynamical MDMS, the correlation performances, error histograms, regression are also provided that endorsed the accuracy.
2. Methodology
3. Results and Discussion
4. Concluding Remarks
- The stochastic artificial intelligence based on Bayesian regularization neural network procedure has never been provided before for the numerical solutions of the MDMS.
- The computing BSR-NNs procedure is implemented to solve three different variations based on the data of training, testing and verification that is respectively given as 75%, 15%, 10%.
- Twelve hidden numbers of neurons have been applied to present the solutions of the nonlinear mathematical system.
- The correctness of the AI based BSR-NNs is observed by using the comparison procedures of the obtained and reference solutions.
- The AE performances in good measures enhance the precision and exactness of the scheme for solving the model.
- The achieved results have been presented to authenticate the efficiency of the artificial intelligence enhanced by the Bayesian regularization neural networks using the regression/correlation, state transitions and error histograms.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | Particulars | Values | Range |
---|---|---|---|
Resting mortality rate (MR) to go in ovipositional places | 0.5 | 0.30–0.56 | |
Pupae MR | 0.4 | 0.22–0.52 | |
MR of eggs | 0.5 | 0.32–0.8 | |
Mature larvae rate into pupae | 0.12 | 0.08–0.17 | |
Resting MR mosquitoes | 0.0043 | 0.03–0.01 | |
Host mosquitos using the latent conditions | 0.46 | 0.322–0.6 | |
Dependent density rate-based larvae mortality | 0.02 | 0–1 | |
Female eggs located per ovipositional | 60 | 50–300 | |
Density-independent based larvae MR | 0.4 | 0.30–0.58 | |
MR Mosquito using the ovipositional sites | 0.41 | 0.41–0.56 | |
Rate of pupae growth into mature | 0.7 | 0.33–1 | |
Ovipositional rate | 3.2 | 3–4 | |
Mosquitoes MR using the hosts penetrating | 0.18 | 0.12–0.23 | |
Eggs rate into larvae | 0.4 | 0.33–1 |
Parameter | Settings |
---|---|
Maximum epochs | 200 |
Fitness | 0 |
Hidden neurons | 12 |
Setting up Mu | 0.25 |
Increasing performances of Mu | 14 |
Adaptive Mu performances | 6 × 10−2 |
Validation fail amount | 8 |
Highest mu values | 109 |
Minimum values of gradient | 10−8 |
Train data | 75% |
Verification statics | 10% |
Test performances | 15% |
Sample selection | Random |
Output/input/hidden values | Single |
Dataset generation | Runge-Kutta |
Other | Default |
Case | MSE | Epoch | Gradient | Performance | Mu | Time | |
---|---|---|---|---|---|---|---|
Test | Train | ||||||
1 | 5.504 × 10−10 | 4.942 × 10−10 | 3 | 5.74 × 10−8 | 4.94 × 10−10 | 0.0500 | 4 |
2 | 6.064 × 10−11 | 7.220 × 10−11 | 5 | 8.98 × 10−8 | 7.22 × 10−11 | 5 | 3 |
3 | 3.775 × 10−11 | 3.544 × 10−11 | 6 | 2.23 × 10−8 | 3.54 × 10−11 | 50 | 2 |
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Suantai, S.; Sabir, Z.; Raja, M.A.Z.; Cholamjiak, W. A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System. Fractal Fract. 2022, 6, 604. https://doi.org/10.3390/fractalfract6100604
Suantai S, Sabir Z, Raja MAZ, Cholamjiak W. A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System. Fractal and Fractional. 2022; 6(10):604. https://doi.org/10.3390/fractalfract6100604
Chicago/Turabian StyleSuantai, Suthep, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, and Watcharaporn Cholamjiak. 2022. "A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System" Fractal and Fractional 6, no. 10: 604. https://doi.org/10.3390/fractalfract6100604
APA StyleSuantai, S., Sabir, Z., Raja, M. A. Z., & Cholamjiak, W. (2022). A Stochastic Bayesian Neural Network for the Mosquito Dispersal Mathematical System. Fractal and Fractional, 6(10), 604. https://doi.org/10.3390/fractalfract6100604