Abstract
This paper is concerned with a design method for modeling Incremental Granular Model (IGM) based on Linguistic Model (LM) and Polynomial Regression (PR) from data set obtained by complex yacht hydrodynamics. For this purpose, we develop a systematic approach to generating automatic fuzzy rules based on Context-based Fuzzy C-Means (CFCM) clustering. This clustering algorithm builds information granules in the form of linguistic contexts and estimates the cluster centers by preserving the homogeneity of the clustered data points associated with the input and output space. Furthermore, IGM deals with localized nonlinearities of the complex system so that the modeling discrepancy can be compensated. After performing the design of 2nd order PR as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the presented IGM showed a better performance in comparison to the previous works for predicting the hydrodynamic performance of sailing yachts.
Highlights
During the last decade, it is advantageous to combine several computing techniques synergistically, rather than exclusively, resulting in the construction of complementary hybrid intelligent systems in confronting real-world application domains such as modeling, control, and optimization for complex engineering problems [1]
The studies associated with this clustering approach are Linguistic Model (LM) [5], RBFN(Radial Basis Function Networks)-Context-based Fuzzy C-Means (CFCM) [8], LM with learning [9], TSK(Takagi-Sugeno-Kang)-LM [10], and Incremental Model (IM) [11]
We develop the Incremental Granular Model (IGM) for predicting complex hydrodynamic performance of sailing yacht
Summary
It is advantageous to combine several computing techniques synergistically, rather than exclusively, resulting in the construction of complementary hybrid intelligent systems in confronting real-world application domains such as modeling, control, and optimization for complex engineering problems [1]. Among these approaches, the neuro-fuzzy and soft computing as a computational approach to learning and machine intelligence is a widely used computing framework based on the concepts of fuzzy model, neural networks, data clustering, and several stochastic optimization methods for computational intelligence.
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