Fuzzy modeling of nonlinear pH processes through neural approach

This paper is concerned with the modeling and identification of nonlinear 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 network-based self-organizing algorithms. Two typical pH processes were treated including a weak acid-strong base system and a two-output system with buffering taking part in reaction. Simulation results have shown that these nonlinear pH-processes can be modeled reasonably well by the present schemes which are simple but efficient.<
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