Abstract
In this paper, we develop a genetically oriented rule-based Incremental Granular Model (IGM). The IGM is designed using a combination of a simple Linear Regression (LR) model and a local Linguistic Model (LM) to predict the modeling error obtained by the LR. The IGM has been successfully applied to various examples. However, the disadvantage of IGM is that the number of clusters in each context is determined, with the same number, by trial and error. Moreover, a weighting exponent is set to the typical value. In order to solve these problems, the goal of this paper is to design an optimized rule-based IGM with the use of a Genetic Algorithm (GA) to simultaneously optimize the number of cluster centers in each context, the number of contexts, and the weighting exponent. The experimental results regarding a coagulant dosing process in a water purification plant, an automobile mpg (miles per gallon) prediction, and a Boston housing data set revealed that the proposed GA-based IGM showed good performance, when compared with the Radial Basis Function Neural Network (RBFNN), LM, Takagi–Sugeno–Kang (TSK)-Linguistic Fuzzy Model (LFM), GA-based LM, and IGM itself.
Highlights
A considerable number of studies have been performed regarding Fuzzy Models (FM), along with a rapid growth in the variety of real-world applications [1]
We shall demonstrate the performance of the Genetic Algorithm (GA) in the optimization design of the Incremental Granular Model (IGM)
We apply the proposed GA-based IGM to a coagulant dosing process in a water purification plant, automobile mpg prediction, and a Boston housing data set as real-world problems
Summary
A considerable number of studies have been performed regarding Fuzzy Models (FM), along with a rapid growth in the variety of real-world applications [1]. It has been realized that real-world problems with complex and nonlinear characteristics require hybrid intelligent models that integrate methodologies, architecture, and techniques from various models. In confronting real-world application problems, it is advantageous to integrate several computing techniques synergistically rather than exclusively, resulting in the design of complementary hybrid intelligent systems. The representative method frequently used in conjunction with hybrid intelligent systems is neuro-fuzzy inference modeling [2]. Neural Networks (NN) adapt themselves to cope with changing environments and have learning characteristics. Fuzzy inference systems incorporate the knowledge of human expertise and perform fuzzy reasoning and knowledge-based decision-making.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
