Structured Learning and Decomposition of Fuzzy Models for Robotic Control Applications
Journal of Intelligent and Robotic Systems
Fuzzy Logic-A Modern Perspective
IEEE Transactions on Knowledge and Data Engineering
Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
Learning fuzzy rules from iterative execution of games
Fuzzy Sets and Systems - Theme: Modeling and learning
A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology
Fuzzy Sets and Systems - Theme: Learning and modeling
Neuro-fuzzy system with learning tolerant to imprecision
Fuzzy Sets and Systems - Theme: Learning and modeling
Affine TS-model-based fuzzy regulating/servo control design
Fuzzy Sets and Systems
Producing interpretable local models in parametric CMAC by regularization
International Journal of Knowledge-based and Intelligent Engineering Systems
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
Fuzzy Modelling Methodologies for Large Database
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
Rapid load following of an SOFC power system via stable fuzzy predictive tracking controller
IEEE Transactions on Fuzzy Systems
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
LMI based design of constrained fuzzy predictive control
Fuzzy Sets and Systems
On-line design of takagi-sugeno models
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Mathematics and Computers in Simulation
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
SparseFIS: data-driven learning of fuzzy systems with sparsity constraints
IEEE Transactions on Fuzzy Systems
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Expert Systems with Applications: An International Journal
A TSK fuzzy inference algorithm for online identification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Analysis of the TaSe-II TSK-Type fuzzy system for function approximation
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Brief A dynamic fuzzy model for a drum-boiler-turbine system
Automatica (Journal of IFAC)
On employing fuzzy modeling algorithms for the valuation of residential premises
Information Sciences: an International Journal
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
An effective 2-stage method for removing impulse noise in images
Journal of Visual Communication and Image Representation
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The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. We propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user's preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example