River flow estimation using adaptive neuro fuzzy inference system
Mathematics and Computers in Simulation
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An expert system for predicting aeration performance of weirs by using ANFIS
Expert Systems with Applications: An International Journal
The adaptive neuro-fuzzy model for forecasting the domestic debt
Knowledge-Based Systems
Forecasting stock market short-term trends using a neuro-fuzzy based methodology
Expert Systems with Applications: An International Journal
An adaptive neuro-fuzzy model for prediction of student's academic performance
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock trading with cycles: A financial application of ANFIS and reinforcement learning
Expert Systems with Applications: An International Journal
Prediction of building energy needs in early stage of design by using ANFIS
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparison of different input selection algorithms in neuro-fuzzy modeling
Expert Systems with Applications: An International Journal
Policy-enhanced ANFIS model to counter SOAP-related attacks
Knowledge-Based Systems
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Welfare analysis is a very important issue in the context of socio-economic. There is a huge literature about economic welfare and exist many conventional techniques and traditional methods that are used in almost all economic welfare studies. Regardless of the method used in welfare studies, the key element to promote the welfare of households in a community is to identify factors affecting welfare. For the first time, Adaptive Network Based Fuzzy Inference System (ANFIS) which is a powerful artificial intelligence tool is used as a new approach in economic welfare. To do this, real micro-data including some characteristics of households and housing units were used. We identified the most important factors affecting household welfare applying the ANFIS method. The ANFIS was then parameterized using these factors in order to predict the welfare measure. The empirical results showed that the ANFIS method outperforms the multiple regression method.