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

Li, Zairan; He, Ting; Cao, Luying; Wu, Tunhua; McCauley, Pamela; Balas, Valentina E.; Shi, Fuqian

doi:10.3233/ifs-151932

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…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.…”

Section: Paper Multi-sensor Information Fusion Methods Based On Bp Neumentioning

confidence: 99%

Multi-sensor Information Fusion Method Based on BP Neural Network

2016

Int. J. Onl. Eng.

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Abstract-This study aims to solve the problem of multisensor information fusion, which is a key issue in the multisensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

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Section: Paper Multi-sensor Information Fusion Methods Based On Bp Neumentioning

confidence: 99%

Multi-sensor Information Fusion Method Based on BP Neural Network

2016

Int. J. Onl. Eng.

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“…Li et al proposed a Gaussian fuzzy inference model for multi-source homogeneous data [26]. The model uses three indicators (center representation, population, and density function) to describe single-source information and adopts a mixed Gaussian model to represent multi-source fusion information.…”

Section: Related Workmentioning

confidence: 99%

A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data

Zhang

Qin

2022

Systems

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Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping heterogeneous fuzzy data to a unified distribution space by adjusting the mean and variance of data from each information source. As a result of combining the normalized descriptions from various sources for an object, we can obtain a fused representation of that object. We then derive an adaptive Gaussian-shaped membership function based on the addition law of the Gaussian distribution. GFIS uses it to dynamically granulate fusion inputs and to design inference rules. This proposed membership function has the advantage of being able to adapt to changing information sources. Finally, we integrate the normalization method and adaptive membership function to the Takagi–Sugeno (T–S) model and present a modified fuzzy inference framework. Applying our methodology to four datasets, we confirm that the data do lend support to the theory implying the improved performance and effectiveness.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Diabetic plantar pressure analysis using image fusion

Cao

Dey

Ashour

et al. 2018

Multimed Tools Appl

Self Cite

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Plantar pressure images analysis is the key issue of designing comfortable shoe products through last customizing system, which has attracted the researchers' curiosity toward image fusion as an application of medical and industrial imaging. In the current work, image fusion has been applied using wavelet transform and compared with Laplace Pyramid. Using image fusion rules of Mean-Max, we presented a plantar pressure image fusion method employing haar wavelet transform. It was compared in different composition layers with the Laplace pyramid transform. The experimental studies deployed the haar, db2, sym4, coif2, and bior5.5 wavelet basis functions for image fusion under decomposition layers of 3, 4, and 5. Evaluation metrics were measured in the case of the different layer number of wavelet decomposition to determine the best decomposition level and to evaluate the fused image quality using with different wavelet functions. The best wavelet basis function and decomposition layers were selected through the analysis and the evaluation measurements. This study established that haar wavelet transform with five decomposition levels on plantar pressure image achieved superior performance of 89.2817% mean, 89.4913% standard deviation, 5.4196 average gradient, 14.3364 spatial frequency, 5.9323 information entropy and 0.2206 cross entropy.

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

Cited by 9 publications

References 34 publications

Multi-sensor Information Fusion Method Based on BP Neural Network

Multi-sensor Information Fusion Method Based on BP Neural Network

A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data

Diabetic plantar pressure analysis using image fusion

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