A novel approach to fuzzy wavelet neural network modeling and optimization. (January 2015)

• Record Type: Journal Article
• Authors: Cheng, Rong; Bai, Yanping
• Is Part Of: International journal of electrical power & energy systems. Volume 64(2015:Jan.)
• Page Start: 671
• Page End: 678
• Publication Date: 2015-01
• Subjects: Fuzzy logic -- Wavelet neural network -- Particle swarm optimization -- Gradient descent algorithm -- System identification; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2014.07.067
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel approach to fuzzy wavelet neural network modeling is presented. A modified PSO algorithm named inline-PSO is proposed to optimize parameters. Gradient descent algorithm is combined with inline-PSO. Efficiency has been validated by examples of dynamic system identification. Abstract: In this paper, an efficient approach of combining Takagi–Sugeno–Kang fuzzy system with wavelet based neural network is presented. The model replaces the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model with wavelet neural network (WNN), thus each rule uses fuzzy set to separate the input space into subspaces spanned by different wavelet functions. For finding the optimal values for parameters of our proposed fuzzy wavelet neural network (proposed-FWNN), a hybrid learning algorithm integrating an improved particle swarm optimization (PSO) and gradient descent algorithm is employed. The two-layer inline-PSO process is proposed in this paper, whose adjustment scheme is more fitting the consequent pattern learning based gradient descent optimization and will locate a good region in the search space. Simulation examples are given to test the efficiency of proposed-FWNN model for identification of the dynamic plants. It is seen that our modeling and optimization approach results in a better performance.