Modeling and analysis of the simplest fuzzy PID controller of Takagi–Sugeno type with modified rule base

Raj, Rishi; Mohan, B.M.

doi:10.1007/s00500-017-2674-8

Cited by 15 publications

(2 citation statements)

References 33 publications

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“…It is essential to mention that the ANFIS model mainly consists of five functional blocks. The Sugeno model (Figure 4) has a rule base that takes the form shown below [75,76]. The ANFIS model was integrated with the thermal and RGB indices to predict the measured plant traits and was developed and trained using a GA approach based on a population algorithm.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis) Modelmentioning

confidence: 99%

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Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

et al. 2021

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Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis) Modelmentioning

confidence: 99%

“…It is essential to mention that the ANFIS model mainly consists of five functional blocks. The Sugeno model (Figure 4) has a rule base that takes the form shown below [75,76]. Through putting a = A1 and b = B1:…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis) Modelmentioning

confidence: 99%