Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

2015 ◽  
Vol 125 (3-4) ◽  
pp. 541-554 ◽  
Author(s):  
Hadi Memarian ◽  
Mohsen Pourreza Bilondi ◽  
Majid Rezaei
Keyword(s):  
Uncertainty Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Drought Prediction ◽  
System Validation ◽  
Neuro Fuzzy

scholarly journals Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

2019 ◽  
Vol 13 (1) ◽  
pp. 878-891 ◽  
Author(s):  
Sinan Q. Salih ◽  
Mohammed Falah Allawi ◽  
Ali A. Yousif ◽  
Asaad M. Armanuos ◽  
Mandeep Kaur Saggi ◽  
...  
Keyword(s):  
Process Simulation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Model ◽  
Evaporation Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Nasser Lake

Adaptive Neuro-Fuzzy Inference System in Agriculture

2020 ◽  
pp. 130-153
Author(s):  
K. Aditya Shastry ◽  
Sanjay H. A.
Keyword(s):  
Crop Yield ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Yield Prediction ◽  
Inference System ◽  
Fourth Section ◽  
The Third ◽  
Neuro Fuzzy ◽  
Basic Concepts

This chapter emphasizes the use of adaptive fuzzy inference system (ANFIS) in agriculture. An overview of the basic concepts of ANFIS is provided at the beginning, where the underlying architecture of ANFIS is also discussed. The introduction is followed by the second section which highlights the diverse applications of ANFIS in agriculture during recent times. The third section describes how Matlab software can be utilized to build the ANFIS model. The fourth section describes the case study of the application of ANFIS for crop yield prediction. The conclusion follows this case study.

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