Given the nonlinear feature of a photovoltaic generator, a maximum power point tracking algorithm (MPPT) is required in a photovoltaic system leading to maximum power point (MPP) operation and maximizing the power generated. The tracking MPP techniques are based on an actual or estimated research mechanism using experimental data. Conventional MPPT techniques like perturbe and observe (P&O), incremental conductance, etc., are good enough to track the maximum power for the PV systems, but they are less stable, more oscillating around the MPP. Generally, techniques based on the estimated research mechanisms, such as the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Radial Basis Function Neural Network (RBFNN), etc., are supervised automatic learning techniques, which aims to create a model for an unknown function in order to find a relationship between input data and output data. In the case of RBF Neural Network, the center of the radial base function, the variance of the radial function base and the weight must be chosen. If these variables are not chosen appropriately, the RBF neural network can degrade the validity and accuracy of the modeling. On the other hand the RBF network suffers from a growth in the size of the hidden layer comparable to that of a set of learning data which also implies more computational time. The solution of these two problems is the motivation of this research. The PSO algorithm is used to optimize the parameters of the RBFNN by introducing a new adaptive strategy of particle swarm optimizer to dynamically adjust the inertia weight factor co and the new velocity v(td)(t + 1) with a new mu coefficient. The obtained results based on RBFNN hybrid approach with PSO (PSO-RBFNN approach) were compared with the results obtained with the adaptive Neuro-Fuzzy Inference System (ANFIS). The experimental test bench of the PSO-RBFNN approach has been implemented using a MyRio card, which prove the good performances of the new proposed technique in terms of the average relative errors of the learning, test and control data, for the model PSO-RBFNN which converge approximately to 0.26%, 0.294% and 0.8% respectively, and energy efficiency MPPT in the case of atmospheric parameters varying over time can reach 99.04%.