Abstract
This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.
Highlights
Various studies have been conducted on complex real-world problems with nonlinear characteristics
We propose a method to optimize the number of clusters and fuzzy coefficients, which are internal parameters of the context-based fuzzy C-means (CFCM) clustering method that models the local part of the incremental granular model (IGM)
In order to solve these problems, we propose IGM which optimizes the number of clusters and fuzzy coefficients using particle swarm optimization
Summary
Various studies have been conducted on complex real-world problems with nonlinear characteristics. Since the traditional GM method described above generates the same cluster for each context, it is difficult to obtain good prediction performance for problems with strong nonlinear characteristics To solve these problems, we studied optimization of internal parameters using the genetic algorithm (GA), an evolutionary optimization algorithm. Bashir [42] proposed a wavelet method using PSO-based artificial neural network. Catalao [46] proposed a short-term electricity pricing model using hybrid wavelet-PSO-ANFIS. We propose a method to optimize the number of clusters and fuzzy coefficients, which are internal parameters of the CFCM clustering method that models the local part of the IGM with particle swarm optimization (PSO), which is a natural imitative optimization algorithm.
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