Modeling pH neutralization processes using fuzzy-neural approaches

This paper is concerned with the modeling and identification of pH-processes via fuzzy-neural approaches. A simplified fuzzy model acting as an approximate reasoner is used to deduce the model output on the basis of the identified rule-base which is derived by using one of the following three network-based self-organizing algorithms: unsupervised self-organizing counter-propagation network (USOCPN), supervised self-organizing counter-propagation network (SSOCPN), and self-growing adaptive vector quantization (SGAVQ). Three typical pH processes were treated including a strong acid-strong base system, a weak acid-strong base system, and a two-output system with buffering taking part in reaction. Extensive simulations including on-line modeling have shown that these nonlinear pH-processes can be modeled reasonably well by the present schemes which are simple but efficient.