Application of ANFIS to Agricultural Economic Variables Forecasting Case Study: Poultry Retail Price

Fahimifard, Seyed Mohammad; Salarpour, M; Sabouhi, M; Shirzady, S.

doi:10.3923/jai.2009.65.72

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…And also learning & generalization of the training data are possible with the use of ANFIS (Krueger et al, 2011). A feed forward neural network structure, having each layer as a component of neural-fuzzy system is associated with ANFIS (Fahimifard et al, 2009). Serge (2001) has depicted the strength of ANFIS as its capability to execute linguistic concepts and to find nonlinear relationships between input and output.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Mishra¹,

Satapathy

Jain³

2022

International Journal of Service Science, Management, Engineering, and Technology

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In the present study, 13 anthropometric hand dimensions, hand grip strength, push strength, and pull strength of 90 male farmers of Odisha in India were statistically analyzed and then, fuzzy logic toolbox of MATLAB version 2010 was used in order to create the fuzzy inference system (FIS) using ANFIS. The mean hand grip strength with standard deviation was found to be 255.21 N ± 75.46. The average push strength in standing posture for farmers was found to be 193.12 N ± 76.12, whereas pull strength in standing posture was 200.59 N ± 64.02. Very high correlation coefficient i.e. 0.977, 0.994 and 0.990 was obtained between “hand length and hand grip strength”, “hand breadth with thumb and push strength”, and “hand length and pull strength”, respectively. Finally, from the obtained ANFIS models for the prediction of muscle strength, it was concluded that ANFIS could well predict the farmers muscle strength with minimum errors. This will help to evaluate muscle capabilities to avoid musculoskeletal disorders, and in ergonomic design of tools and equipment as a health-care initiative.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Mishra¹,

Satapathy

Jain³

2022

International Journal of Service Science, Management, Engineering, and Technology

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“…His research stated that nonlinear models overcome the linear models strongly. Fahimifard et al (2009) studied the application of ANFIS in Iran's poultry retail price forecasting in contrast with ARIMA model. Their findings stated that ANFIS outperforms the ARIMA model in all three 1, 2 and 4 weeks ahead.…”

Section: Introductionmentioning

confidence: 99%

Application of NNARX to agriculture sector value added forecasting: A case of Iran’s agriculture sector

Fahimifard¹

2012

Afr. J. Agric. Res.

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This study firstly uses the Cobb-Douglas production function and Auto-Regression Distributed Lag (ARDL) approach for estimating the long-run function of Iran's agriculture sector value added and then compares the forecasting performance of specified ARDL model with Neural Network Auto-Regressive model with eXogenous inputs (NNARX) using forecasting performance criteria (R 2 , MAD and RMSE). The results of ARDL specification indicated that 1% increase in labor, capital and energy factors will increase Iran's agriculture sector value added 0.36, 0.23 and 0.32%, respectively. Also, the results of forecast performance criteria show that NNARX nonlinear model forecasting performance for Iran's agriculture sector value added is better in contrast with the ARDL linear model because (1) The Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) divided are less than 1 and (2) The R2 divided is more than 1. Therefore, according to the importance of the agriculture sector as the main alimentary source for mankind, accurate prediction of agriculture sector value added for its using new NNARX model is strongly recommended to the agriculture sector policy makers.

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“…Results from generated artificial intelligence methods are clearly showing ANFIS advantage for fitting the measured data [14] [15]. Fig.…”

Section: Comparisonmentioning

confidence: 99%

Standalone Application Using JAVA and ANFIS for Predicting Electric Energy Consumption Based on Forecasted Temperature

Mlakić¹,

Nikolovski²,

Vucinic³

2016

Proceedings of the 27th International DAAAM Symposium 2016

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Predicting energy consumption is the ability, which is useful anywhere where the energy resource is the main product. In particular, the electric energy is a very expensive product to be wasted without a concrete need. The need for fast prediction of the electric energy consumption is based on various inputs covering the following aspects: any time, any sampling resolution, any depth based on well-known and available data. Today, the advancing of the technology allows the new generation of software applications to integrate different methods for the prediction of the electric energy consumption, which is based on various available data coming from anywhere. Focus of this paper is flexibility of AI methods in predicting energy consumption and availability of input data for predicament. In this paper, the method for predicting el. energy consumption is based on a daily temperature using the Adaptive Neuro Fuzzy Inference System (ANFIS). Comparison of results are presented in paper between ANN and ANFIS on same input data, and that puts ANFIS method as best for predicting energy consumption. The results analysis is done with the developed MatLab software and the data acquisition module is specifically developed in JAVA. The data source is the web service available as "weather underground" web service. All the components are integrated in a performing and compact lightweight application that supports mobility, easy to use and easy to change functionality. The modelling, testing and validation are made on the data acquired from the electric energy consumption provided by the distribution system of the town on Kreševo in Bosnia and Hercegovina.

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Application of ANFIS to Agricultural Economic Variables Forecasting Case Study: Poultry Retail Price

Cited by 20 publications

References 12 publications

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Application of NNARX to agriculture sector value added forecasting: A case of Iran’s agriculture sector

Standalone Application Using JAVA and ANFIS for Predicting Electric Energy Consumption Based on Forecasted Temperature

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