ANFISAdaptive Network-Based Fuzzy Inference System
ANFISAdaptive Neuro-Fuzzy Inference System
ANFISAdaptive Network Fuzzy Inference System
References in periodicals archive ?
FSVRN model and Jang's ANFIS model [21] with 400 training data are listed in Table 1.
ANFIS techniques (Chiu 1996) outlined above have allowed to perform some experiments whose results were reported in Tables 1-3.
2011) to develop ANFIS, ANN and FIS-based prediction models for the ultimate bearing capacity.
ANFIS is more effective than conventional classifiers such as multiple linear regressions in classifying patterns in which the input is noisy and the system is not well defined.
The main aim of this paper is to investigate the capability of an ANFIS in modeling gold price changes and to evaluate its performance in comparison with ANN and other traditional time series modeling techniques such as ARIMA.
A synthetic data would be generated and presented to ANFIS to classify between the normal and attack traffic.
For controlling the temperature and humidity of an HVAC system using PID controllers, an intelligent approach for modeling and the control of the system was achieved by Soyguder and Alli (2009b), using ANFIS, leading, in particular, to a more accurate prediction of damper gap rate and faster and simplified solutions.
ANFIS was designed as a single output system, thus each ANFIS system can only successfully diagnose one type of machine fault [14][15].
An option is to update the decision boundaries to match the raw-material quality in smaller regions, for example by applying ANFIS (Adaptive Neuro Fuzzy Inference Systems) (Jang 1993).
The idea of fuzzy control of the parallel robots was tested and demonstrated in ANFIS MATLAB environment tool [5].
In this paper, a multi-objective optimization method, based on combination of ANFIS and ACO evolutionary algorithms, is proposed to obtain the optimal parameters in turning processes.