A Novel Data Fusion Algorithm Based on Fuzzy Similarity Degree

The novel data fusion algorithm based on fuzzy similarity degree represents an intelligent processing method for multi-source heterogeneous data. This algorithm calculates the fuzzy similarity degree between different data sources and dynamically adjusts fusion weights, effectively addressing the precision degradation issues caused by data conflicts or noise in traditional fusion methods.

The core methodology consists of three stages: First, raw data undergoes fuzzification through membership functions to eliminate dimensional differences; Second, an improved similarity formula (such as hybrid metrics combining Hamming distance and Euclidean distance) evaluates inter-source similarity; Finally, an adaptive weighting model is constructed based on similarity calculation results to enhance fusion of high-confidence data.

Key MATLAB implementation aspects focus on matrix-based fuzzy operations, leveraging MATLAB's vector computation advantages to significantly improve execution efficiency in big data scenarios. Implementation typically involves creating custom functions for membership degree calculation using array operations, and optimizing similarity computation through built-in distance functions (pdist2 for Hamming/Euclidean distances). The algorithm demonstrates approximately 15%-30% fusion accuracy improvement over traditional weighted averaging methods in applications like sensor network data fusion and multimodal medical image analysis.

Important considerations: Practical deployment requires adjusting fuzzy membership function parameters according to specific data distributions. For strongly nonlinear data relationships, incorporating similarity correction factors is recommended. Future extensibility includes integration with deep learning for automated optimization of fuzzy rules, potentially implemented through MATLAB's Deep Learning Toolbox for neural network-based rule adjustment.