Application of resilient propagation neural network in determination of trace chromium with oscillographic chronopotentiometry

A resilient propagation neural network (RPNN) was first applied in the determination of trace chromium in the passivation solution of copper foil with oscillographic chronopotentiometry, The effect of parameters about RPNN on the prediction results was discussed. Experimental results showed that the incision depth of Cr(III) on dE/dt-E curve was rectilinearly related to the concentration of Cr(III)) in the range of 4.0 x 10(-7) to 1.3 x 10(-6) mol/L, and the detection limit for determination of Cr(III) was 8 x 10(-8) mol/L. Compared with the application of standard BP neural network technique to the oscillographic chronopotentiometric determination, RPNN has the advantage of higher predication accuracy and faster convergent rate.