This is not a useful method. LDA is basically a two-class classifier, which works under certain assumptions on the distribution of the data. If those assumptions hold, LDA is optimal and there is no reason to use a neural network. If those assumptions don't hold, LDA might not be a useful tool. Even if you do use LDA, LDA maps each data point to one of two classes, so there is no information left for the neural network to do any further processing.
In short: LDA is not useful as a preprocessing step before a classifier; you either use LDA as the classifier itself, or you don't use LDA at all.