Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications.

Simple Summary: A radial basis function neural network (RBFNN) is proposed for identifying and diagnosing non-linear systems. The neural network developed was optimized not only for RBFNN output layer parameters, including centers, width, and weights, but also for network size (the count of neurons stored in the hidden layer). Two optimization methods, namely, particle swarm optimization (PSO) and hybrid PSO coupled with a spiral-shaped mechanism (HPSO-SSM), were used to train the RBFNN hyperparameters and network size. Simulation results showed that the suggested technique outperformed other current approaches with respect to prediction precision and network compactness. A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) and robust hybrid particle swarm optimization (HPSO) approach. A PSO is coupled with a spiral-shaped mechanism (HPSO-SSM) to optimize the PSO performance by mitigating its constraints, such as sluggish convergence and the local minimum dilemma. Three advancements are incorporated into the hypothesized HPSO-SSM algorithms to achieve remarkable results. First, the diversity of the search process is promoted to update the inertial weight ω based on the logistic map sequence. Then, two distinct parameters are trained in the original position update algorithm to enhance the work efficiency of the successive generation. Finally, the proposed approach employs a spiral-shaped mechanism as a local search operator inside the optimum solution space. Moreover, the HPSO-SSM method concurrently improves the RBFNN parameters and network size, building a model with a compact configuration and higher precision. Two non-linear benchmark functions and the total phosphorus (TP) modelling issue in a waste water treatment process (WWTP) are utilized to assess the overall efficacy of the creative technique. The results of testing indicate that the projected HPSO-SSM-RBFNN algorithm performed very effectively. [ABSTRACT FROM AUTHOR]