Neurocomputations in Relational Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A supervisory fuzzy neural network control system for tracking periodic inputs
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy-set based models of neurons and knowledge-based networks
IEEE Transactions on Fuzzy Systems
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A novel learning algorithm for recurrent neurofuzzy networks is introduced in this paper. The learning algorithm uses the gradient projection method to update the network weights. Moreover, the core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. The neural network topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent neurofuzzy network is verified via examples of nonlinear system modeling and time series prediction. The results confirm the effectiveness of the neurofuzzy network.