Journal: Scientific Reports

Article Title: An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

doi: 10.1038/s41598-022-11565-2

Figure Lengend Snippet: Standard ANFIS architecture. Number of Story Points and Project Velocity are given as inputs. These inputs have been derived from 21 projects dataset (dataset sample given in Table ). ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs. Different inputs (as feature pairs) tested against Actual Time. The feature pair with minimum error has been chosen in input layer.

Article Snippet: ANFIS based exhaustive search MATLAB functionality has been used to decide the inputs.

Techniques: Derivative Assay

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:

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:

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:

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:

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

Journal: MethodsX

Article Title: Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area

doi: 10.1016/j.mex.2022.101959

Figure Lengend Snippet: Structure of an ANFIS model.

Article Snippet: More details of the ANFIS can be found in other literature , such us the number of layer and rules, type of membership functions and learning algorithm This ANFIS model is obtained by Matlab software and those parameters are choose by an optimization techniques based on the tolerance error fixed at 10 − 4 .

Techniques:

Journal: MethodsX

Article Title: Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area

doi: 10.1016/j.mex.2022.101959

Figure Lengend Snippet: ANFIS structure generate by matlab.

Article Snippet: More details of the ANFIS can be found in other literature , such us the number of layer and rules, type of membership functions and learning algorithm This ANFIS model is obtained by Matlab software and those parameters are choose by an optimization techniques based on the tolerance error fixed at 10 − 4 .

Techniques: