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This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules.
On the basis of the estimation of condition aggravation rate by using fuzzy rules and membership functions, we obtain values for each of the above listed partial condition indices.
FLCs transform numerical information into linguistic values and infer output control responses by using fuzzy rules that encapsulate nonlinear relationships between the system inputs and controlled outputs without the need for any mathematical model.
Different from black-box methods, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms by using fuzzy rules.
Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules.
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Even though engineering and applied sciences deal with numerical data, they have successfully implemented fuzzy logic by using fuzzy rule based (FRB) systems by verbalizing data.
The agents' behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area.
It applies fuzzy reasoning mechanisms to obtain outputs and carries out the computation using fuzzy rules; defuzzification: done by a defuzzification module which transforms fuzzy outputs back to crisp values.
The algorithm is developed using fuzzy rules and directional techniques.
Then, the local controllers are combined using fuzzy rules to form a fuzzy controller.
A preliminary forecast is done using NWP and quality is estimated using fuzzy rules.
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by utilizing fuzzy rules
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