tool artificial neural network (anfis) Search Results


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MathWorks Inc adaptive neuro-fuzzy inference system (anfis)
RMSE values obtained from <t> ANFIS </t> via BBD approach.
Adaptive Neuro Fuzzy Inference System (Anfis), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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RMSE values obtained from <t> ANFIS </t> via BBD approach.
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The fundamental configuration of <t>ANFIS.</t>
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The fundamental configuration of <t>ANFIS.</t>
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The fundamental configuration of <t>ANFIS.</t>
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The fundamental configuration of <t>ANFIS.</t>
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Image Search Results


RMSE values obtained from  ANFIS  via BBD approach.

Journal: Heliyon

Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

doi: 10.1016/j.heliyon.2024.e25813

Figure Lengend Snippet: RMSE values obtained from ANFIS via BBD approach.

Article Snippet: Adaptive Neuro-Fuzzy Inference System (ANFIS), an artificial neural network based on the Takagi-Sugano fuzzy inference system, is a MATLAB application to make estimations depending on functions prepared by training fuzzy logic with a given data set.

Techniques:

RMSE values obtained via Regression model and ANFIS prediction at optimum conditions.

Journal: Heliyon

Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

doi: 10.1016/j.heliyon.2024.e25813

Figure Lengend Snippet: RMSE values obtained via Regression model and ANFIS prediction at optimum conditions.

Article Snippet: Adaptive Neuro-Fuzzy Inference System (ANFIS), an artificial neural network based on the Takagi-Sugano fuzzy inference system, is a MATLAB application to make estimations depending on functions prepared by training fuzzy logic with a given data set.

Techniques:

Train and test data vs FIS output obtained with optimum ANFIS model (trimf 6-6-3).

Journal: Heliyon

Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

doi: 10.1016/j.heliyon.2024.e25813

Figure Lengend Snippet: Train and test data vs FIS output obtained with optimum ANFIS model (trimf 6-6-3).

Article Snippet: Adaptive Neuro-Fuzzy Inference System (ANFIS), an artificial neural network based on the Takagi-Sugano fuzzy inference system, is a MATLAB application to make estimations depending on functions prepared by training fuzzy logic with a given data set.

Techniques:

Predicted data obtained with optimum ANFIS model vs. experimental data.

Journal: Heliyon

Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

doi: 10.1016/j.heliyon.2024.e25813

Figure Lengend Snippet: Predicted data obtained with optimum ANFIS model vs. experimental data.

Article Snippet: Adaptive Neuro-Fuzzy Inference System (ANFIS), an artificial neural network based on the Takagi-Sugano fuzzy inference system, is a MATLAB application to make estimations depending on functions prepared by training fuzzy logic with a given data set.

Techniques:

The comparison of other  ANFIS  modeling studies on Cr(VI) adsorption.

Journal: Heliyon

Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

doi: 10.1016/j.heliyon.2024.e25813

Figure Lengend Snippet: The comparison of other ANFIS modeling studies on Cr(VI) adsorption.

Article Snippet: Adaptive Neuro-Fuzzy Inference System (ANFIS), an artificial neural network based on the Takagi-Sugano fuzzy inference system, is a MATLAB application to make estimations depending on functions prepared by training fuzzy logic with a given data set.

Techniques: Comparison, Adsorption, Modification, Concentration Assay

The fundamental configuration of ANFIS.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: The fundamental configuration of ANFIS.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques:

ANFIS model variables interactions.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: ANFIS model variables interactions.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques:

 ANFIS  processing settings.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: ANFIS processing settings.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques: Generated

Graphical plot of ANFIS model training sets.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: Graphical plot of ANFIS model training sets.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques:

Graphical plot of ANFIS model testing sets.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: Graphical plot of ANFIS model testing sets.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques:

ANFIS Membership function plots.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: ANFIS Membership function plots.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques:

Statisical computations to evaluate the performance of the developed model.

Journal: Scientific Reports

Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

doi: 10.1038/s41598-024-77144-9

Figure Lengend Snippet: Statisical computations to evaluate the performance of the developed model.

Article Snippet: The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid intelligent system that blends neural network and fuzzy logic functionalities to perform adaptive and accurate inference, especially in modeling complex and nonlinear systems .

Techniques: