Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting

Benmouiza, Khalil; Cheknane, Ali

doi:10.1007/s00704-018-2576-4

Cited by 116 publications

(52 citation statements)

References 38 publications

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“…In ANFIS models, during construction phase to initiate modeling an initial fuzzy model is required. This initial fuzzy model will decide the selection of input features, selecting the type and number of MFs for inputs, and division of input space into rules (Benmouiza & Cheknane, ). In this research work, an initial fuzzy model is derived based on the fuzzy if‐then rules formed by the clustering methods.…”

Section: Modelingmentioning

confidence: 99%

“…The authors (Sehgal & Garg, ) have mentioned that the Expectation Maximization (EM) algorithm will take a long time to form clusters. In the latest research conducted by Benmouiza & Cheknane (), different ANFIS models using FCM, SC, and GP have been studied to predict hourly solar radiation. The author (Dewi, ) has studied and compared the performance of K‐Means with FCM clustered ANFIS models for weather forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

Phate

Ramanathan

Palanisamy

2019

J Food Process Engineering

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In the present research work, a clustered adaptive neuro‐fuzzy inference system (ANFIS) is used to develop the model of relationship between the weight with 1D and 2D features of the Indian sweet lime fruit extracted through the developed computer vision system. Initially, three clustering methods are used to classify the input data into a homogeneous group. Then, the self‐learning proficiency of an artificial neural network and the ability of fuzzy theory to deal with the uncertainty and nonlinearity are utilized by developing the ANFIS model. Finally, the clustered data is combined with the ANFIS models trained by two different learning methods are analyzed. The goodness‐of‐fit indicators close to unity and the lower values of the error performance metrics implies the adequacy, robustness, and superiority of the proposed method. Moreover, the lower value of Theil's coefficient for uncertainty (UII) signifies the credibility of the model to predict the weight of a sweet lime fruit. Practical applications The presented improved approach of clustered ANFIS modeling for weighing sweet lime fruit will be fruitful to the researchers, farmers, and engineers. In order to, design and develop the real time weight‐based grading, sorting, and packaging machine's knowledge about the fruit weight‐size relation is a basic requirement. Use of this proposed approach (computer vision linked with the clustered ANFIS model) will greatly reduce the handling of the fruit as well as the workload that are responsible for the qualitative and quantitative losses of fruits on bulk industrial scale. Also, this weighing model using 1D and 2D features will be more beneficial, faster, and economically viable substitute to the on‐field manual method used in agriculture specially during the sorting, grading, and packaging of sweet lime.

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Section: Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

Phate

Ramanathan

Palanisamy

2019

J Food Process Engineering

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“…For instance, Mohaddes et al [ 8 ] used ANFIS to forecast Iran’s agricultural product export, Zheng et al [ 14 ] in their work used for short-term wind power prediction. Benmouiza et al [ 3 ] used ANFIS with subtractive clustering and greed partitioning for an hour-ahead solar radiation forecasting. ANFIS architecture uses both artificial neural networks and fuzzy logic.…”

Section: Methodsmentioning

confidence: 99%

A Comparison Between Adaptive Neuro-fuzzy Inference System and Autoregressive Integrated Moving Average in Predicting COVID-19 Confirmed Cases in Bangladesh

Parvez

Rakin

Zaman

et al. 2020

Lecture Notes in Networks and Systems

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Since December 2019, the novel coronavirus (COVID-19) has become one of the most contagious diseases to have hit the world for several decades. From December 2019 till May 2020, this respiratory syndrome-like disease has quickly spread to all countries around the world and has taken more than 400 thousand lives. The WHO declared a global pandemic situation due to the virus from March 2020. The source of this virus is not known, especially since there are no well-placed standards for its diagnosis and treatment. Several factors are involved in the spread of the disease. There have been several studies to predict or forecast the number of new cases in upcoming dates. In our study, we tested the widely used ANFIS-Adaptive Neuro-Fuzzy Inference System and the ARIMA-Autoregressive Integrated Moving Average methods to predict the total number of COVID-19 cases in the

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“…A number of clustering methods such as fuzzy c-means (FCM) (Bezdek, 1981), subtractive clustering (Yager and Filev, 1994), and grid partitioning (Giotis and Giannakoglou, 1998) can be used to get membership functions when creating a FIS. These clustering methods allow the grouping of features into groups with each group having similar properties that help to discern the correlation between the data thus simplifying the prediction process (Benmouiza and Cheknane, 2018). For each clustering method, two different FIS models (Mamdani-type FIS and Sugeno-type FIS) have been developed (Nayak et al, 2013).…”

Section: Au0summentioning

confidence: 99%

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Abimbola

Mittelstet

Messer

et al. 2020

Science of The Total Environment

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Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting

Cited by 116 publications

References 38 publications

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

A Comparison Between Adaptive Neuro-fuzzy Inference System and Autoregressive Integrated Moving Average in Predicting COVID-19 Confirmed Cases in Bangladesh

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

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