1. Rajesh Kumar "Memory Recurrent Elman Neural Network-Based Identification of Time-Delayed Nonlinear Dynamical System," published in IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022), DOI: 10.1109/TSMC.2022.3186610, the Impact factor (I.F) = 8.7, SCIE.
2. Rajesh Kumar "A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems." Published in Applied Soft Computing, Elsevier (2022): 108836, I.F = 6.6, SCIE.
3. Rajesh Kumar and Smriti Srivastava. "A novel dynamic recurrent functional link neural network-based identification of nonlinear systems using Lyapunov stability analysis," published in Neural Computing and Applications, Springer, Vol. 33, Pages: 7875–7892 (2021) (SCOPUS).
4. Rajesh Kumar, and Smriti Srivastava. "Externally Recurrent Neural Network-based identification of dynamic systems using Lyapunov stability Analysis," published in ISA Transactions, Elsevier, 98 (2020): 292–308, I.F. = 6.5, SCIE.
5. Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, and Amit Mohindru. "Temporally local recurrent radial basis function network for modelling and adaptive control of nonlinear systems," published in ISA Transactions, Elsevier, Volume 87, April 2019, Pages 88–115, I.F. = 6.5, SCIE.
6. Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, and Amit Mohindru. "Self-recurrent wavelet neural network-based identification and adaptive predictive control of nonlinear dynamical systems," published in International Journal of Adaptive Control and Signal Processing, Wiley, Volume 32, Issue 9: 1326–1358, 2018, I.F. = 2.5, SCIE.
7. Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, and Amit Mohindru. "Comparative study of neural networks for dynamic nonlinear systems identification," published in Soft Computing, Springer, 23, (2019): 101–114, I.F = 2.5, SCIE.
8. Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, and Amit Mohindru. "Diagonal recurrent neural network-based identification of nonlinear dynamical systems with Lyapunov stability-based adaptive learning rates," published in Neurocomputing, Elsevier, 287 (2018), 102-117, I.F. = 6.5, SCIE.
9. Rajesh Kumar, Smriti Srivastava, and J. R. P. Gupta. "Comparative Study of Neural Networks for Control of Nonlinear Dynamical Systems with Lyapunov Stability-Based Adaptive Learning Rates," published in Arabian Journal for Science and Engineering, Springer, 43.6 (2018): 2971–2993, I.F. = 2.9, SCIE.
10. Rajesh Kumar, Smriti Srivastava, and J. R. P. Gupta. "Diagonal recurrent neural network-based adaptive control of nonlinear dynamical systems using Lyapunov stability criterion." published in ISA Transactions, Elsevier, Volume 67, 2017, Pages 407–427, I.F = 6.5, SCIE.
11. Rajesh Kumar, Smriti Srivastava, and J. R. P. Gupta. "Lyapunov stability-based control and identification of nonlinear dynamical systems using adaptive dynamic programming," published in Soft Computing, Springer, 21.15 (2017): 4465–4480, I.F. = 2.5, SCIE.
12. Rajesh Kumar, Smriti Srivastava, and J. R. P. Gupta. "Modeling and adaptive control of nonlinear dynamical systems using radial basis function network." published in Soft Computing, Springer, 21.15 (2016): 4447–4463, I.F. = 2.5, SCIE.
13. Rajesh Kumar, Smriti Srivastava, and J. R. P. Gupta. "Online modelling and adaptive control of robotic manipulators using Gaussian radial basis function networks," published in Neural Computing and Applications, Springer, 30.1 (2016): 223-239 (SCOPUS).
14. Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta, and Amit Mohindru. "Lyapunov stability- Back dynamic back propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems" published in Soft Computing, Springer, 24, pages 5463–5482 (2020), I.F = 2.5, SCIE.
15. Anuli Dass, Smriti Srivastava, and Rajesh Kumar. "A novel Lyapunov-stability-based recurrent-fuzzy system for the Identification and adaptive control of nonlinear systems," published in Applied Soft Computing - Elsevier (2023): 110161, I.F. = 6.6, SCIE.
16. Tanvi Gupta, Rajesh Kumar, "A novel feed-through Elman neural network for predicting the compressive and flexural strengths of eco-friendly jarosite mixed concrete: design, simulation, and a comparative study," Soft Computing, Springer (2023), I.F. = 2.5. SCIE.
17. Rajesh Kumar, "Double internal loop higher-order recurrent neural network-based adaptive control of the nonlinear dynamical system" published in Soft Computing-Springer (2023): 1-19, I.F. = 2.5, SCIE.
18. R. Shobana, Bhavnesh Jaint, and Rajesh Kumar "Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems." Published in Soft Computing-Springer (2023), I.F. = 2.5, SCIE.
19. S Srivastava, Rajesh Kumar "Design and application of a novel higher-order type-n fuzzy-logic-based system for controlling the steering angle of a vehicle: a soft computing approach" published in Soft Computing-Springer (2023), I.F. = 2.5, SCIE.
20. P. Verma, N. Malhotra, R. Suri, and Rajesh Kumar, "Automated smart artificial intelligence-based proctoring system using deep learning." Published in Soft Computing-Springer (2023), I.F. = 2.5, SCIE.
21. S Chaturvedi, N Kumar, and Rajesh Kumar, "A PSO optimised novel PID Neural Network model for Temperature Control of Jacketed CSTR: Design, Simulation, and a Comparative Study," published in Soft Computing-Springer (2023), I.F. = 2.5, SCIE.
22. S. Chaturvedi, N. Kumar, and Rajesh Kumar, "Two Feedback PID Controllers Tuned with Teaching–Learning Based Optimization Algorithm for Ball and Beam Systems," published in IETE Journal of Research—Taylor Francis Journal (2023), I.F. = 1.3, SCIE.
23. Rajesh Kumar, Shefali Srivastava, Anuli Dass, and Smriti Srivastava. "A novel approach to predict stock market price using a radial basis function network." International Journal of Information Technology (2019), Springer: 1–9 (SCOPUS).
24. Raman Tiwari, Rajesh Kumar, Rajat Gera, and Smriti Srivastava. "On comparing the performances of MLP and RBFN on the sales forecasting problem." International Journal of Information Technology (2019), Springer: 1–9 (SCOPUS).
25. R Shobana, Rajesh Kumar, and B. Jaint, "A recurrent neural network-based identification of complex nonlinear dynamical systems: a novel structure, stability analysis and a comparative study," published in Soft Computing-Springer (2023), I.F. = 2.5, SCIE.
26. Rajesh Kumar, "Recurrent Context Layered Radial Basis Function Neural Network for the Identification of Nonlinear Dynamical Systems," Neurocomputing, Elsevier (2024), I.F. = 6.5, SCIE.
27. Kartik Saini, Narendra Kumar, Bharat Bhushan, and Rajesh Kumar, "Artificial neural network-based adaptive control for nonlinear dynamical systems," International Journal of Adaptive Control and Signal Processing, Wiley (2024), I.F. = 3.8, SCIE.
28. R. Shobana, Rajesh Kumar, and B. Jaint, "Nonlinear dynamical system approximation and adaptive control based on Hybrid-Feed-Forward Recurrent Neural Network: simulation and stability Analysis," Expert Systems-Wiley (2024), I.F. = 2.3, SCIE.
29. Rajesh Kumar, Smriti Srivastava, and Amit Mohindru, "Nonlinear Dynamic Engineering Processes Modeling using a Lyapunov-Stability-Based novel locally connected recurrent Pi-Sigma neural network: Design, Simulation, and a Comparative Study," Evolving Systems—Springer (2024), I.F. = 2.7, SCIE.
30. Kartik Saini, Narendra Kumar, Bharat Bhushan, and Rajesh Kumar. "Nonlinear complex dynamic system identification based on a novel recurrent neural network." Soft Computing-Springer (2025): 1-20, I.F. = 2.5, SCIE.
31. Rajesh Kumar "New recurrent weighted Lyapunov-stability-based brain emotional learning-based neural network: Application to modelling the nonlinear dynamical systems" published in Circuits, Systems, and Signal Processing, Springer (2025), I.F. = 2.0, SCIE.
32. Rajesh Kumar, "A stable framework-based modeling of the complex dynamical system using a double context layered with a self-weighted output feedback loop in the Elman recurrent neural network," Information Sciences, Elsevier (2025): 122132, I.F. = 6.8, SCIE.
33. Richa Sahu, Rajesh Kumar, and Smriti Srivastava. "Nonlinear Complex Dynamical Systems Modeling and Adaptive Control Based on a Novel Diagonally Expanded Functional Link Neural Network (DEFLNN)" Circuits, Systems, and Signal Processing (2025): 1-27, I.F. = 2.0, SCIE.
34. Richa Sahu, Smriti Srivastava, and Rajesh Kumar, "An Adaptive Learning Rate Based Novel Recurrent Neural Network Modeling and Control of Complex Non‐Linear Dynamical Systems", published in International Journal of Adaptive Control and Signal Processing-Wiley (2025), I.F. = 3.8, SCIE.
35. Kartik Saini, Narendra Kumar, Bharat Bhushan, and Rajesh Kumar, "Local Recurrent Sigma Pi Artificial Neural Network‐Based Adaptive Control of Nonlinear Dynamical Systems." Optimal Control Applications and Methods—Wiley (2026), Impact Factor (I.F) = 1.5, SCIE.
36. Rajesh Kumar, "A novel recurrent neural network with robust learning algorithm: Application to modeling of nonlinear complex systems." Neurocomputing-Elsevier (2025): 132214, I.F. = 6.5, SCIE.
37. Rajesh Kumar, "DEJRNN: A novel fusion recurrent neural network-based complex dynamical system modeling and the convergence analysis. " Applied Soft Computing-Elsevier (2025): 114243, I.F. = 6.6, SCIE.
38. Richa Sahu, Rajesh Kumar, and Smriti Srivastava. "Dynamic system identification based on a novel pi-sigma neural network with Lyapunov stability analysis. " ISA Transactions-Elsevier (2025), I.F. = 6.5, SCIE.
39. Shobana, R., Rajesh Kumar, and Bhavnesh Jaint. "Feedback-based optimization of feed-forward neural network for the modeling of complex nonlinear dynamical systems using novel APSOBP algorithm." ISA Transactions-Elsevier (2025), I.F. = 6.5, SCIE.
40. Kartik Saini, Narendra Kumar, Bharat Bhushan, and Rajesh Kumar, “Adaptive Neural Architecture Reduction for Robust Modeling of Complex Systems,” International Journal of Adaptive Control and Signal Processing (2026): 1–16, I.F. = 3.8, SCIE.
41. Kartik Saini, Narendra Kumar, Bharat Bhushan, and Rajesh Kumar, “Modeling and Control of Nonlinear Systems Using an Improved Adaptive Moment Optimization Approach” International Journal of Adaptive Control and Signal Processing (2026): 1–16, I.F. = 3.8, SCIE.