System identification: theory for the user
System identification: theory for the user
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Attention to time in fuzzy logic
Fuzzy Sets and Systems
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Memory neuron networks for identification and control of dynamical systems
IEEE Transactions on Neural Networks
Compromise ratio method for fuzzy multi-attribute group decision making
Applied Soft Computing
Expert Systems with Applications: An International Journal
Optimization of Fuzzy Membership Function Using Clonal Selection
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Extracting Fuzzy Linguistic Summaries Based on Including Degree Theory and FCA
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
ACS'08 Proceedings of the 8th conference on Applied computer scince
Optimization of multiple input-output fuzzy membership functions using clonal selection algorithm
Expert Systems with Applications: An International Journal
Reinforcement learning-based tuning algorithm applied to fuzzy identification
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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New approaches in fuzzy modelling in order to solve practical limitations found in classic adaptive fuzzy modelling, are considered an interesting contribution in the fuzzy logic field. In this work, an approach for the development of dynamical fuzzy models is presented. The approach allows to incorporate the system dynamics into the fuzzy membership functions, which are defined in terms of the sample mean value and the variance of each variable of the fuzzy model from the input and output values of the system to be modelled. These fuzzy membership functions, defined as dynamical functions, have adjustable parameters which are adapted by using a conventional off-line gradient descent-based algorithm. In this way, after the learning process, the resulted fuzzy model has non-static membership functions dependent on the available values of the variables at time t. Some application examples to illustrate the performance of the proposed dynamical adaptive fuzzy model on system identification are presented, and the experimental results are discussed in order to remark the capabilities of the new fuzzy model.