Efficient Data-Driven Modeling with Fuzzy Relational Rule Network
نویسندگان
چکیده
An algorithmic approach for efficient identification of Fuzzy Relational Rule Network (FR N) from data is presented. FR N uses a relational input partition for human-understandable modeling of linear interactions between the input variables. Mutual subsethood has been used to estimate the optimal interaction structure. An analytical relationship between the mutual subsethood measure and one of the parameters of the membership functions is now derived. The use of this relationship results in a dramatic speed-up of the identification process.
منابع مشابه
A fuzzy relational rule network modeling of electromyographical activity of trunk muscles in manual lifting based on trunk angels, moments, pelvic tilt and rotation angles
The main objective of the study was to model the electromyographic (EMG) responses for 10 trunk muscles in manual-lifting tasks using the fuzzy relational rule network (FRRN). The FRRN utilized trunk-related variables, including sagittal and lateral trunk moments, pelvic tilt and pelvic rotation angles, and sagittal, lateral, and twist trunk angles as model inputs. The EMG data for model traini...
متن کاملComparing Methods for Knowledge-Driven and Data-Driven Fuzzy Modeling: A Case Study in Textile Industry
The aim of this study is to compare different approaches to fuzzy systems design from different perspectives: knowledge-driven versus data-driven and rule-based (flat) versus tree-based (hierarchical). More specifically, our comparison is focused on two of the arguably most important criteria in fuzzy systems design, namely accuracy and interpretability. We compare two approaches to data-driven...
متن کاملA Solution to the Problem of Extrapolation in Car Following Modeling Using an online fuzzy Neural Network
Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree le...
متن کاملA comparison between knowledge-driven fuzzy and data-driven artificial neural network approaches for prospecting porphyry Cu mineralization; a case study of Shahr-e-Babak area, Kerman Province, SE Iran
The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising a...
متن کاملData-Driven Fuzzy Modeling: Transparency and Complexity Issues
Recently, the interest in data-driven approaches to the modeling of nonlinear processes has increased. Techniques based on fuzzy sets and rule-based systems have proven suitable mainly because of their potential to yield transparent models that are at the same time reasonably accurate. Many of the data-driven fuzzy modeling algorithms, however, aim primarily at good numerical approximation, whi...
متن کامل