Comparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting

Rodríguez, Elen Yanina Aguirre; Gamboa, Alexander Alberto Rodriguez; Rodríguez, Elias Carlos Aguirre; Silva, Aneirson Francisco da; Rizol, Paloma Maria Silva Rocha; Marins, Fernando Augusto Silva

doi:10.1109/tla.2022.9885166

Cited by 3 publications

(3 citation statements)

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“…It means that when the covera has a high value, the specificity has a low value. A rational information granule can expressed by Equation (15), and this equation is referred to as the performance index (P Coverage is a criterion to indicate whether data are included in the information granules generated by the GFM, and it shows the degree to which the output values are included in the range of the triangular information granules. When Y k is a triangular context, it has a value close to 1 if y k is included in Y k = y − k , y + k and a value close to 0 if y k is not included.…”

Section: 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = ∑ 𝑒𝑥𝑝(−|𝑦 − 𝑦 |) (mentioning

confidence: 99%

“…It means that when the coverage has a high value, the specificity has a low value. A rational information granule can be expressed by Equation (15), and this equation is referred to as the performance index (PI).…”

Section: Speci F Icitymentioning

confidence: 99%

“…A complementary model, called a neuro-fuzzy system, was proposed by combining the advantages of fuzzy models and neural networks to address the abovementioned issues [1][2][3][4][5]. Studies are actively being conducted on neuro-fuzzy inference systems [6][7][8][9][10][11][12][13][14][15]. Similar to FIS, neuro-fuzzy inference systems are expressed by fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

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A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

Yeom,

Kwak

2023

Applied Sciences

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This paper is concerned with the design of a context-based fuzzy C-means (CFCM)-based multi-granular fuzzy model (MGFM) with hierarchical tree structures. For this purpose, we propose three types of hierarchical tree structures (incremental, aggregated, and cascaded types) in the design of MGFM. In general, the conventional fuzzy inference system (FIS) has problems, such as time consumption and an exponential increase in the number of if–then rules when processing large-scale multivariate data. Meanwhile, the existing granular fuzzy model (GFM) reduces the number of rules that increase exponentially. However, the GFM not only has overlapping rules as the cluster centers become closer but also has problems that are difficult to interpret due to many input variables. To solve these problems, the CFCM-based MGFM can be designed as a smaller tree of interconnected GFMs. Here, the inputs of the high-level GFMs are taken from the output to the low-level GFMs. The hierarchical tree structure is more computationally efficient and easier to understand than a single GFM. Furthermore, since the output of the CFCM-based MGFM is a triangular fuzzy number, it is evaluated based on a performance measurement method suitable for the GFM. The prediction performance is analyzed from the automobile fuel consumption and Boston housing database to present the validity of the proposed approach. The experimental results demonstrate that the proposed CFCM-based MGFM based on the hierarchical tree structure creates a small number of meaningful rules and solves prediction-related problems by making them explainable.

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Section: 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = ∑ 𝑒𝑥𝑝(−|𝑦 − 𝑦 |) (mentioning

confidence: 99%

Section: Speci F Icitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

Yeom,

Kwak

2023

Applied Sciences

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A decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company

Aguirre-Rodríguez,

Rodríguez,

da Silva

et al. 2024

Int. j. inf. tecnol.

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Comparative Analysis of ANFIS and State‐ANFIS for Forecasting Cooking Oil Prices Based on Processed Palm Oil Yield (Crude Palm Oil)

Ulama,

Hibatullah,

Habibi

et al. 2024

Applied Computational Intelligence and Soft Computing

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The adaptive neuro‐fuzzy inference system (ANFIS) is widely employed in modeling intricate systems, especially in forecasting cooking oil prices. However, ANFIS confronts limitations stemming from backpropagation, prompting the exploration of alternatives like particle swarm optimization (PSO). Hybrid PSO‐ANFIS models exhibit enhanced forecasting accuracy, albeit at the expense of increased computational time. Nonetheless, both ANFIS and hybrid PSO‐ANFIS encounter challenges in handling dynamic relationships influenced by macroeconomic factors. To address these issues, the development of the State‐ANFIS (S‐ANFIS) method integrates regime‐switching models, enhancing its capability to manage dynamic relationships. Particularly effective in cooking oil price prediction, S‐ANFIS clarifies the impact of external variables and improves forecast accuracy and interpretability by combining ANFIS with state‐space models. Our analysis underscores S‐ANFIS’s superiority over ANFIS, particularly with Gaussian membership functions, as it reduces RMSE and MAPE values by half while requiring fewer nodes, thereby improving computational efficiency. Additionally, integrating key state variables like crude palm oil (CPO) prices, inflation rates, and the USD exchange rate enhances the reliability of the model. Overall, S‐ANFIS offers a more accurate, interpretable, and efficient approach to forecasting cooking oil prices, demonstrating superior predictive capabilities.

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Comparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting

Cited by 3 publications

References 0 publications

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

A decision-making framework with machine learning for transport outsourcing based on cost prediction: an application in a multinational automotive company

Comparative Analysis of ANFIS and State‐ANFIS for Forecasting Cooking Oil Prices Based on Processed Palm Oil Yield (Crude Palm Oil)

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