A new approach of adaptive Neuro Fuzzy Inference System (ANFIS) modeling for yield prediction in the supply chain of Jatropha

2010 ◽  
Author(s):  
S.P. Srinivasan ◽  
P. Malliga
Keyword(s):  
Supply Chain ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Yield Prediction ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system

2003 ◽  
Vol 32 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Dursun Kaya ◽  
A. Samet Hasiloglu ◽  
Mahmut Bayramoglu ◽  
Hakki Yesilyurt ◽  
A. Fahri Ozok
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anthropometric Measurements ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

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.

Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 1 (2) ◽  
pp. 300 ◽  
Author(s):  
Mohamed Salah El-Din Abdel Aziz ◽  
Mohamed Elsamahy ◽  
Mohamed Moustafa ◽  
Fahmy Bendary
Keyword(s):  
Design Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Impedance Measurements ◽  
New Approach ◽  
Line Voltage ◽  
Artificial Intelligent ◽  
Neuro Fuzzy ◽  
Generator Terminal

<em>This paper presents a new approach for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a 345kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R &amp; X) and the generator RMS Line to Line voltage and Phase current (Vtrms &amp; Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other technique. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process and the obtained results are very promising</em>.

scholarly journals ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION

2017 ◽  
Vol 15 (2) ◽  
pp. 95-102
Author(s):  
O A OJESANMI ◽  
A D ADEKOYA ◽  
A A AWOSEYI
Keyword(s):  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Minimum Temperature ◽  
Fuzzy Inference ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Yield Prediction ◽  
Yield Data ◽  
Inference System ◽  
Neuro Fuzzy

This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro-fuzzy inference system in the prediction of okra yield using environmental parameters such as minimum temperature, relative humidity, evaporation, sunshine hours, rainfall and maximum temperature as input into the neuro-fuzzy inference system, and yield as output. The agro meteorological data used were obtained from the department of agro meteorological and water management, Federal University of Agriculture, Abeokuta and the yield data were obtained from the Department of Horticulture, Federal University of Agriculture, Abeokuta. MATLAB was used for the analysis of the data. From the results, the maximum predicted yield showed that at minimum temperature of 24.4 oc, relative humidity of 78.3% and evaporation of 5.5mm, the yield predicted is 1.67 tonnes/hectare. 

scholarly journals Rice yield prediction using adaptive Neuro-fuzzy inference system (ANFIS)

2020 ◽  
Vol 8 (1) ◽  
pp. 1638-1640
Author(s):  
Dr. M Kalpana ◽  
Dr. B Sivasankari ◽  
Dr. P Prema ◽  
Dr. R Vasanthi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rice Yield ◽  
Yield Prediction ◽  
Inference System ◽  
Neuro Fuzzy
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