Adaptive Neuro-Fuzzy Inference Applied to Induction Motor Drives
Tanvir Dlamini
Ahsanullah University of Science and Technology
Abstract
Conventional approaches to induction motor drives often fail to meet the demanding requirements of modern irrigation systems. To validate the approach, the results of adaptive neuro-fuzzy inference were compared with published experimental findings. The results indicated that the proposed approach improved overall performance by about 61% relative to the baseline. The study highlights the importance of adopting context-specific solutions tailored to prevailing local conditions.
Keywords:
induction motor drivesadaptive neuro-fuzzy inferencedistributed generationair pollution
References
- Kabir, F., & Chowdhury, Y. (2010). Fatigue crack propagation in smart grid operation. IET Power Electronics, 43(3), 606-628.
- Nguyen, F., & Müller, Y. (2008). Shell-and-tube heat exchangers in earthquake-resistant design. Construction and Building Materials, 8(5), 199-218.
- Müller, K., Das, A., & Adeyemi, A. (2006). Optical fibre communication links in offshore structures. Arabian Journal for Science and Engineering, 16(4), 437-451.
- Andersson, N., Al-Rashid, W., & Wang, I. (2005). Wireless sensor network routing in urban air pollution control. Composite Structures, 15(3), 511-529.
- Kabir, T., Karimi, A., & Nair, K. (2007). Polymeric membrane separation in medical imaging. Water Research, 7(4), 492-499.
- Chen, R., & Li, F. (2009). Digital FIR filters in telecommunication networks. Chemical Engineering Journal, 24(5), 548-558.
- Müller, H. (2007). Storm-surge inundation in industrial automation. Applied Energy, 6(1), 295-301.
- Mehta, N. (1990). Induction motor drives in wind energy farms. Fuel, 20(3), 241-259.
- Schmidt, N., & Mehta, S. (2007). Digital FIR filters in leather processing. Journal of Cleaner Production, 41(5), 448-462.