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. 

Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

2019 ◽  
Vol 234 (3) ◽  
pp. 438-451 ◽  
Author(s):  
Mostafa Jalal ◽  
Poura Arabali ◽  
Zachary Grasley ◽  
Jeffrey W Bullard
Keyword(s):  
Compressive Strength ◽  
Relative Humidity ◽  
Silica Fume ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Criteria ◽  
System Model ◽  
Rubberized Concrete ◽  
Inference System ◽  
Neuro Fuzzy

Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.

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.

scholarly journals Using fuzzy logic for estimating monthly pan evaporation from meteorological data in Emara/ South of Iraq

2012 ◽  
Vol 9 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Inference System

Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed to evaluate effect of each of these variables on estimation process. Two error statistics namely root mean squared error and coefficient of determination were used to measure the performance of the developed models. The results indicated that the model, whose input variables are T, W, and RH, perform the best for estimating evaporation values. In addition, the model which is dominated by (T) is significantly and distinctly helps to prove the predictive ability of fuzzy inference system. Furthermore, agreements of the results with the observed measurements indicate that fuzzy logic is adequate intelligent approach for modeling the dynamic of evaporation process.

scholarly journals Solar radiation forecasting in nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach

2019 ◽  
Vol 9 (5) ◽  
pp. 3916 ◽  
Author(s):  
Sani Salisu ◽  
Mohd Wazir Mustafa ◽  
Mamunu Mustapha ◽  
Olatunji Obalowu Mohammed
Keyword(s):  
Solar Radiation ◽  
Fuzzy Inference ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Inference System ◽  
Hybrid Approaches ◽  
Neuro Fuzzy ◽  
Sunshine Hours ◽  
Statistical Indicators

<p>For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO and WT algorithms is efficient for solar radiation forecasting even though the hybrid WT-ANFIS gives more accurate results.</p>

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

scholarly journals Comparative Study of Daily Rainfall Forecasting Models Using Adaptive-Neuro Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 10 (2) ◽  
pp. 529-536 ◽  
Author(s):  
M. A Sojitra ◽  
P. A Pandya
Keyword(s):  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Daily Rainfall ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Forecasting Models ◽  
Mean Temperature ◽  
Neuro Fuzzy

The study was carried out to develop rainfall forecasting Models. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for developing Models rainfall of Udaipur city. Two data sets were prepared using 35 year of weather parameters i.e. wet bulb temperature, mean temperature, relative humidity and evaporation of previous day and previous moving average week were used to prepare case I and case II respectively. Gaussian and Generalized Bell membership functions were used to prepare models. Statistical and hydrologic performance indices of ANFIS (Gaussian, 5) gave better performance among developed four models. The study showed that sensitivity analysis revealed wet bulb temperature is most sensible parameter followed by mean temperature, relative humidity and evaporation.

Sign in / Sign up

Export Citation Format

Share Document