Adaptive Neuro-Fuzzy Inference System (ANFIS) Model Development

In the given context, we can employ the Adaptive Neuro-Fuzzy Inference System (ANFIS) to develop a high-precision model. ANFIS represents an effective machine learning approach that combines fuzzy logic principles with neural network adaptability. The system automatically adjusts its parameters based on input data through a hybrid learning algorithm, enhancing model accuracy through both backpropagation gradient descent and least-squares optimization methods. By implementing ANFIS modeling, we can achieve more accurate prediction outcomes through its five-layer architecture: 1) input fuzzification layer, 2) rule application layer, 3) normalization layer, 4) consequent parameter calculation, and 5) output summation. This structured approach generates a Takagi-Sugeno fuzzy inference system where each node's function adapts during training. The implementation typically involves defining membership functions using MATLAB's genfis1 or genfis2 functions, followed by anfis training with epoch configuration and error tolerance settings. This modeling technique provides valuable insights for research and practical applications, particularly in system identification and predictive modeling scenarios where it effectively handles nonlinear relationships through its neuro-fuzzy framework.