Improved Parameter Identification Method Based on Moving Rate
This work addresses computational efficiency in fuzzy neural networks for applications like weather forecasting and security prediction, though it appears incremental.
The authors tackled the problem of high computational complexity in parameter identification for fuzzy neural networks by proposing an improved T-S fuzzy inference method and parameter identification approach, which reduced learning order, time complexity, and learning error in precipitation forecast and security situation prediction tests.
To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with existing Sugeno fuzzy inference based on Compositional rules and type-distance fuzzy inference method, the proposed fuzzy inference algorithm has a less amount of complexity in calculating and the calculating process is simple. Next, a parameter identification method for FNN based on production inference is proposed. Finally, the proposed method is applied for the precipitation forecast and security situation prediction. Test results showed that the proposed method significantly improved the effectiveness of identification, reduced the learning order, time complexity and learning error.