DENFIS

AcronymDefinition
DENFISDynamic Evolving Neuro-Fuzzy Inference System
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All fuzzy membership functions are triangular functions in DENFIS models.
The DENFIS method was employed for estimating pan evaporation based on the climatic data of maximum and minimum temperatures, solar radiation, wind speed, and relative humidity.
Table 2 reports the periods of training and test data sets used for each DENFIS and ANFIS model.
The optimal Dthr values obtained for the DENFIS models are 0.02, 0.01, and 0.013 for the M1, M2, and M3 data sets, respectively.
The evaluation of the system involved selection of the MF and comparing k-ANFIS performance against performance of ANFIS and DENFIS. The overall results have shown high performance, which summarizes the suitability of neuro-fuzzy inference system for detecting Android malware based on system permissions.
Conventional DENFIS. The conventional DENFIS can be used to generate a streamlined model in NF form for affective design, which cannot be achieved by the modified DENFIS [23, 24], in which a reasonable number of fuzzy rule-based models are generated.
For the rule consequent of each fuzzy rule, a first-order linear model is developed based on a weighted recursive least square, which uses a forgetting factor in the conventional DENFIS [23, 25].
A modified DENFIS is proposed to process a batch of N survey data sets for each update process, where the forgetting factor [lambda] is a variable instead of a constant, such that the decay effect can be controlled by varying the value of [lambda].
The modified DENFIS was developed to decrease the influence of survey data sets by batch over time.
The implementation of DENFIS offline learning process is described by Kasabov and Song (2002).
First, the parameters to be optimized in the DENFIS model include:
What is more, in order to estimate the accuracy of predictions, the DENFIS model outputs three result parameters: