scholarly journals Prototipe Penilaian Kinerja Tenaga Ahli PT. Inacon Luhur Pertiwi Dengan Pendekatan Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 108-122
Author(s):  
Handa Gustiawan
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Primary Data ◽  
Inference System ◽  
Analysis Process ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Data Reading

Inacon Luhur pertiwi PT. as amanagement consulting firm in carrying outits work on the project PNPM Urban withcontract number HK.02.03 / NMC / IBRD /SATKER-PK / 007/2012 dated 10 May 2012.By carrying out quantitative researchmethods, using primary and secondary dataas samples. Primary data retrieved byconducting an observation as anobservation instrument of expertsperformance assessment. Secondary datawas collected by observing the data,reading, studying and quoting from the bookof literature, as well as the resources thatare closely related to this study. The dataobtained will be used for purposes ofdescriptive data analysis process by usingAdaptive Neuro Fuzzy Inference System(ANFIS). ANFIS method is a method thatuses neural networks to implement fuzzyinference system. Fuzzy inference systemused is the fuzzy inference system modelsTagaki-Sugeno-Kang (TSK) withconsideration of simplicity and easycomputation. The result of this research isthe prototipe of expert performanceevaluation which can be implemented atInacon Luhur Pertiwi PT.

scholarly journals Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models

2020 ◽  
Vol 20 (4) ◽  
pp. 1396-1408
Author(s):  
Hüseyin Yıldırım Dalkiliç ◽  
Said Ali Hashimi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Processes ◽  
Inference System ◽  
Daily Streamflow ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996–2007) of the data were used to train them and 30% (2008–2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE = 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

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