SSWN LEARNING ALGORITHMS WITH APPLICATION TO WWTP

State Space Wavelet Network is a specific neural network with non trivial structure. Such a structure implies problems with the network training. In SSWN, during the training process, weights and wavelons parameters are adapted. This paper presents algorithms and methods for optimising the SSWN parameters. During researches Hybrid Distributed Evolutionary Algorithm has been developed and Extended initialisation strategy was applied. Finally it was found that usage of Hybrid Distributed Evolutionary Algorithm with extended initialisation significantly reduce the time of SSWN learning.