A number of options are available for displaying the training results on the screen. Another option for displaying the training results involves a profile for each output. To do this, a graph that shows the network output, the target output and the error for each of the neurons in the output layer is presented. While the network trains, DynaMind 3.0 provides a substantial amount of feedback, the graphic representation of which makes it easy to interpret. Another useful function provided by DynaMind is the ability to pause training, enabling us to change certain parameters and then resume training to see what effect this has on the network.
Virtual memory uses half the available hard-disk space on the current drive.
NeuroDynamX, the creator of DynaMind, indicates that any number of networks trained with different algorithms can be linked together, limited only by memory constraints. In turn, these linked networks can be embedded royalty-free into commercial software applications. The discrete Fourier transform is defined by the formula in Figure 4. This transformation is useful when a preprocessing network is needed to convert a time domain sample into a frequency domain sample and when working with sound classification problems, for example. These filters can also be embedded into commercial programs using the NeuroLink 2.0 library.