Adaptive chaos control via neural networks : From microorganism-driven climate to cancer growth
Ayub Khanayubkdu@gmail.comDepartment of MathematicsBasic Science Research CentreImam Mohammad lbn Saud Islamic UniversityRiyadh, 13318, Kingdom of Saudi Arabia0009-0009-5317-6855View full profile → , Pardeep Kumarpardeep@ip.du.ac.inDepartment of MathematicsIndraprastha College for WomenUniversity of DelhiNew Delhi, Delhi, 110054, India0000-0002-9416-1723View full profile → , Sarita Jhasaritajhakbw.vbu@gmail.comDepartment of MathematicsK. B. Women’s CollegeVinoba Bhave UniversityHazaribag, Jharkhand, 825301, India0000-0002-0428-8860View full profile → , *Tripti AnandCorresponding authortriptimath@spm.du.ac.inDepartment of MathematicsVinoba Bhave UniversityHazaribag, Jharkhand, 825301, India0009-0007-6778-8874View full profile → , Govind Kumar Jhajhagovi@gmail.comDepartment of MathematicsVinoba Bhave UniversityHazaribag, Jharkhand, 825301, India0000-0002-7353-7752View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2422
- Pages:
- 3309–3322
Abstract
This research work investigates how to control the chaos in dissipative dynamical systems. To this aim, we have analyzed the novel systems: one is related to the variation in temperature caused by micro-organisms, and the other corresponds to the proliferation of cancer cells via interaction with healthy host cells and immune effector cells introduced in Kumar et al. [23]. To control the chaos between these two systems, we have developed the anti-synchronization technique based on neural networks which is capable to manage unknown nonlinearities by approximating it withi the time. Further, the effectiveness of the purposed scheme was assessed through numerical simulation and confirmed that anti-synchronisation conditions were rapidly achieved even with the presence of perturbation. This study emphasizes the impact for controlling the chaos by using neural network methods and to obtain the stability in dissipative biological and environmental systems.
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References
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