Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Fuzzy Modeling for Control
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
Fuzzy relational classifier trained by fuzzy clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Look-ahead based fuzzy decision tree induction
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
On pre-processing algorithms for data stream
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
On fuzzy clustering of data streams with concept drift
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
On resources optimization in fuzzy clustering of data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A cluster validity index for hard clustering
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A new hierarchical clustering algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A new method to construct of interpretable models of dynamic systems
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Creating learning sets for control systems using an evolutionary method
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Parallel realisation of the recurrent multi layer perceptron learning
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A new fuzzy classifier for data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Learning in rough-neuro-fuzzy system for data with missing values
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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Neuro-fuzzy systems are eagerly used for classification and machine learning problems. Researchers find them easy to use because the knowledge is stored in the form of the fuzzy rules. The rules are relatively easy to create and interpret for humans, unlike in the case of other learning paradigms e.g. neural networks. The most commonly used neuro-fuzzy systems are Mamdani (linguistic) and Takagi Sugeno systems. There are also logical-type systems which are well suited for classification tasks. In the paper, another type of fuzzy systems is proposed, i.e. multi-input multi-output systems with additional binary relation for greater flexibility. The relation bonds input and output fuzzy linguistic values. Thanks to this, the system is better adjustable to learning data. The systems have multiple outputs which is crucial in the case of classification tasks. Described systems are tested on several known benchmarks and compared with other machine learning solutions from the literature