Dynamic Fuzzy Neural Network with Automated Rule Generation

Dynamic Fuzzy Neural Network (DFNN) is an intelligent system that combines the advantages of fuzzy logic and neural networks, with its core feature being the ability to automatically generate and adjust fuzzy rules. This innovative architecture originates from the research of Professor Wu Shiqian at Tsinghua University, addressing the limitation of traditional fuzzy systems that rely on manual rule formulation based on expert experience.

The system adopts a hierarchical design: the bottom layer processes data features through the self-learning capability of neural networks, while the upper layer constructs a dynamic fuzzy inference mechanism. When receiving new data, the network triggers three key processes: first, partitioning the input space through fuzzification; second, evaluating the coverage completeness of existing rules; and finally, dynamically generating new rules or adjusting weights of existing rules through competitive learning mechanisms. In code implementation, this involves using clustering algorithms like subtractive clustering for input space partitioning and calculating rule activation strengths using Gaussian membership functions.

This dynamic rule generation mechanism offers three major advantages: firstly, adaptability to non-stationary environments where the system can automatically update the rule base when data distribution changes; secondly, elimination of the limitation requiring pre-defined rule libraries in traditional methods; and thirdly, maintenance of model simplicity through rule pruning algorithms to prevent rule explosion. For parameter optimization, the system employs a hybrid learning strategy: antecedent parameters are adjusted through gradient descent methods, while consequent parameters are updated using recursive least squares estimation. The implementation typically involves separate training loops for premise and consequent parameters with adaptive learning rates.

Typical application scenarios include modeling complex systems with time-varying characteristics, such as industrial process control and financial time series prediction. Compared to static fuzzy systems, this architecture demonstrates stronger environmental adaptability and lower maintenance costs, making it particularly suitable for solving real-world problems with ambiguous boundaries and unclear rules. The MATLAB implementation often utilizes adaptive neuro-fuzzy inference system (ANFIS) frameworks with modifications for dynamic rule growth and pruning capabilities.