Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
The impact of poor data quality on the typical enterprise
Communications of the ACM
Inverse fuzzy-process-model based direct adaptive control
Mathematics and Computers in Simulation
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Subtractive clustering based modeling of job sequencing with parametric search
Fuzzy Sets and Systems - Data analysis
Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system
Information Sciences—Informatics and Computer Science: An International Journal
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Higher order fuzzy system identification using subtractive clustering
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
The shape of fuzzy sets in adaptive function approximation
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Modeling and analysis of packing properties through a fuzzy inference system
Journal of Intelligent Manufacturing
Review: Adaptive cruise control look-ahead system for energy management of vehicles
Expert Systems with Applications: An International Journal
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Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields' performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem.