HPC parallel implementation combining NEST Simulator and Python modules

Cluster Computing - The paper presents a supercomputer parallel implementation of a brain inspired model combining a Python module simulating a layer of retina ganglion cells and NEST Simulator for...     

Backpropagation through time training of a neuro-fuzzy controller

The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too.