A SOFT-SENSING MODEL FOR FEEDWATER FLOW RATE USING FUZZY SUPPORT VECTOR REGRESSION
نویسندگان
چکیده
منابع مشابه
A Soft-sensing Model for Feedwater Flow Rate Using Fuzzy Support Vector Regression
Thermal nuclear reactor power is typically evaluated with secondary system calorimetric calculations based on feedwater flow rate measurements. The feedwater flow rate should therefore be measured accurately. Venturi meters are currently used to measure the feedwater flow rate in most pressurized water reactors (PWRs). The long-term operation of a nuclear power plant causes a buildup of corrosi...
متن کاملA Fuzzy Model of Support Vector Regression Machine
Fuzziness must be considered in systems where available information is uncertain. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM). This integration preserves the benefits of SV...
متن کاملSoft Monotonic Constraint Support Vector Regression
This paper proposes a model for learning soft-monotonic regression functions in the presence of imperfect domain knowledge. It proposes an extension to support vector regression (SVR) wherein a new hardness parameter is introduced to configure the degree of monotonicity. The model supports multiple monotonicity constraints over multiple input dimensions simultaneously. The proposed model has be...
متن کاملA QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES
Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective fu...
متن کاملRevenue forecasting using a least-squares support vector regression model in a fuzzy environment
Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nuclear Engineering and Technology
سال: 2008
ISSN: 1738-5733
DOI: 10.5516/net.2008.40.1.069