System identification: theory for the user
System identification: theory for the user
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Maximum likelihood competitive learning
Advances in neural information processing systems 2
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Digital Filter Design Handbook
Digital Filter Design Handbook
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
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Prediction of critical desalination parameters (recovery and salt rejection) of two distinct processes based on real operational data is presented. The proposed method utilizes the radial basis function network using data clustering and histogram equalization. The scheme involves center selection and shape adjustment of the localized receptive fields. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input space. Networks produced by the proposed algorithm have good generalization performance on prediction of non-linear input–output mappings and require a small number of connecting weights. The proposed method was used for the prediction of two different critical parameters for two distinct Reverse Osmosis (RO) plants. The simulation results indeed confirm the effectiveness of the proposed prediction method.