Stationarity and stability of autoregressive neural network processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Soft Computing and Fuzzy Logic
IEEE Software
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)
Adaptive mixtures of local experts
Neural Computation
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Modeling uncertainty in clinical diagnosis using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling gunshot bruises in soft body armor with an adaptive fuzzy system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extending the functional equivalence of radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The equivalence between fuzzy logic systems and feedforward neural networks
IEEE Transactions on Neural Networks
A hybrid linear-neural model for time series forecasting
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
A flexible coefficient smooth transition time series model
IEEE Transactions on Neural Networks
Are artificial neural networks white boxes?
IEEE Transactions on Neural Networks
Equivalence between RAM-based neural networks and probabilistic automata
IEEE Transactions on Neural Networks
A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters
Applied Intelligence
On the use of cross-validation for time series predictor evaluation
Information Sciences: an International Journal
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models. On the one hand, the regime-switching autoregressive paradigm is a recent development in statistical time series modeling, and it includes a set of models closely related to artificial neural networks. On the other hand, we consider fuzzy rule-based systems in the framework of time series analysis. This paper discloses original results establishing functional equivalences between models of these two classes, and hence opens the door to a productive line of research where results and techniques from one area can be applied in the other. As a consequence of the equivalences presented in this paper, we prove the asymptotic stationarity of a class of fuzzy rule-based systems. Simulations based on information criteria show the importance of the selection of the proper membership function.