Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Application of hybrid system control method for real-time power system stabilization
Fuzzy Sets and Systems
Nonlinear Complex Neural Circuits Analysis and Design by q-Value Weighted Bounded Operator
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Modelling dynamic systems using ANFIS
CIS'09 Proceedings of the international conference on Computational and information science 2009
A new methodology to improve interpretability in neuro-fuzzy TSK models
Applied Soft Computing
Equivalences between neural-autoregressive time series models and fuzzy systems
IEEE Transactions on Neural Networks
Interpolation representation of feedforward neural networks
Mathematical and Computer Modelling: An International Journal
Fuzzy decision making based on variable weights
Mathematical and Computer Modelling: An International Journal
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Perception granular computing in visual haze-free task
Expert Systems with Applications: An International Journal
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Demonstrates that fuzzy logic systems and feedforward neural networks are equivalent in essence. First, we introduce the concept of interpolation representations of fuzzy logic systems and several important conclusions. We then define mathematical models for rectangular wave neural networks and nonlinear neural networks. With this definition, we prove that nonlinear neural networks can be represented by rectangular wave neural networks. Based on this result, we prove the equivalence between fuzzy logic systems and feedforward neural networks. This result provides us a very useful guideline when we perform theoretical research and applications on fuzzy logic systems, neural networks, or neuro-fuzzy systems