Trend-Aware-Based Type-2 Vector Fuzzy Neural Network for Nonlinear System Identification

In nonlinear systems, the system response frequently fluctuates with the discrepancy of variation trends over a certain horizon. In this situation, type-2 fuzzy neural networks can be limited in capturing trend features from the point prediction process due to the inherent value mapping, which may suffer from trend blindness. To address this problem, a trend-aware-based type-2 vector fuzzy neural network (TA-T2VFNN) is designed for modeling nonlinear systems. First, a quantization framework with a hybrid encoding strategy is designed to process both the magnitude and trend information of variables. In this strategy, the trend information with the rate and direction of change is represented by vectors instead of scalars, which aims to compensate for the accumulated error caused by insufficient input information. Second, a directional attention mechanism-based vector fuzzy rule is proposed to capture the trend relationship to mimic the local fluctuation patterns. Then, the vector operation is embedded into fuzzy rules for geometric inference, which can obtain the vector features with point and trend. Third, a collaborative feedback learning algorithm is developed to update the magnitude and direction parameters of TA-T2VFNN. Then, the modeling accuracy can be maintained by obtaining trusted output points and output trends. Finally, the effectiveness of TA-T2VFNN is verified by multiple practical applications.