New Learning Algorithm of Neuro-fuzzy-network

Training of neuro-fuzzy-networks by conventional error backpropagation methods introduces considerable computational complexities due to the need for gradient evaluations. In this paper, the concepts coming from the theory of stochastic learning automaton are used. This method eliminates the need for computation of gradients and hence affords a very simple implementation, particularly for implementation on low-end platforms such as personal computers. And the neuro-fuzzy-network training by a learning automaton approach is applied to a nonlinear multi variable system–the three-tank-system. The simulation result is given.