Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Abstract

Abstract. An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems.

Multi-sensor Information Fusion Method Based on BP Neural Network

“…Apart from the aforementioned studies, information fusion, such as wheat varieties classification [17], rule-based Gaussian-shaped fuzzy control inference system [18], intuitionistic fuzzy decision making [19], and gait-based re-identification and recognition [20], can also be utilized in other fields. In the current research, we study the information fusion problem in multi-sensor environments using BPNN.…”

Diabetic plantar pressure analysis using image fusion

“…Furthermore, it can be combined with the last shoes design to effectively reduce the occurrence of foot disease. This has important significance to improve the quality of peoples life [2], [3], [4]. Several artificial intelligences, data mining, and gait studies have been applied for plantar pressure data feature extraction.…”