Multi-sensor data fusion path combining fuzzy theory and neural networks

In the development of automation and intelligent systems, multi-sensor data fusion technology is crucial. However, due to the uncertainty and incompleteness of sensor data, how to effectively fuse these data has always been a challenge. To solve this problem, the study combines fuzzy theory and neural networks to study the process of multi-sensor data transmission and data fusion. Sensor network clustering algorithms based on whale algorithm optimized fuzzy logic and neural network data fusion algorithms based on sparrow algorithm optimized were designed respectively. The performance test results showed that the first node death time of the data fusion algorithm is delayed to 1122 rounds, which is 391 rounds and 186 rounds later than the comparison algorithm, respectively. In the same round, the remaining energy was always greater than the comparison algorithm, and the difference gradually increased. The results indicate that the proposed multi-sensor data fusion path combining fuzzy theory and neural networks has successfully improved network efficiency and node energy utilization, and extended network lifespan.