Intelligent fuzzy configuration channels: A novel self-tuning framework for complex system modeling

To enhance the fuzzy inference capability of Stochastic Configuration Networks (SCNs), we propose a new neuro-fuzzy model based on Fuzzy Stochastic Configuration Networks (F-SCNs). Unlike traditional SCNs, F-SCNs replace hidden layers with Takagi–Sugeno (T–S) fuzzy inference modules, enabling them to process fuzzy input data, generate meaningful fuzzy rules connected to the output layer and perform reasoning more effectively. A key challenge is establishing a framework that ensures robust modeling performance. To address this, we introduce Self-Organizing Fuzzy Stochastic Configuration Networks (SO-FSCNs) with a Hybrid Learning Algorithm (HL-SOFSCN) for nonlinear system modeling. Additionally, we propose a growing-and-pruning productive approach that refines fuzzy rules based on network knowledge and rule firing intensity. The learning performance of fuzzy rules is improved using error correction algorithms with appropriate initial parameters, while redundant rules with low firing strength are eliminated to maintain a compact structure. Furthermore, we develop a hybrid learning algorithm that integrates a least squares method for parameter tuning with an enhanced second-order optimization approach, treating linear and nonlinear parameters separately to improve learning efficiency. The model is validated using artificial datasets, demonstrating that SCNs achieve a satisfactory predictive accuracy compared to alternative models.