Replacing fuzzy systems with neural networks

In this paper, a neural architecture which gives identical TSK fuzzy system is proposed based on the area selection concept in neural network design. Instead of using traditional membership functions for selection the range of operation, the monotonic pair-wire or sigmoidal activation function is used. In the comparison to popular neuro-fuzzy systems, the proposed approach does not require signal normalization or division. This neural system does not need training process. All parameters of constructed neural networks are directly derived from specifications of fuzzy systems.