Fuzzy modeling and control of multilayer incinerator
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Fuzzy Sets and Systems - Special issue on diagnostics and control through neural interpretations of fuzzy sets
A comparative study of similarity measures
Fuzzy Sets and Systems
Application of a similarity measure of fuzzy sets to fuzzy relational equations
Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
Fuzzy lattice neurocomputing (FLN) models
Neural Networks
Self-Organizing Maps
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Fuzzy Relational Systems: Foundations and Principles
Fuzzy Relational Systems: Foundations and Principles
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
A Flexible Similarity Measure for 3D Shapes Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial-Time Metrics for Attributed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence)
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Information Sciences: an International Journal
A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Editorial: Genetic and evolutionary computing
Information Sciences: an International Journal
Information Sciences: an International Journal
Development of fuzzy and control charts using α-cuts
Information Sciences: an International Journal
Grouping fuzzy sets by similarity
Information Sciences: an International Journal
IEEE Transactions on Knowledge and Data Engineering
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Editorial: Modelling uncertainty
Information Sciences: an International Journal
An introduction to morphological perceptrons with competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Advances and challenges in interval-valued fuzzy logic
Fuzzy Sets and Systems
Parallel and multistage fuzzy inference based on families of α-level sets
Information Sciences: an International Journal
Information Sciences: an International Journal
Microgenetic algorithms as generalized hill-climbing operators forGA optimization
IEEE Transactions on Evolutionary Computation
Size reduction by interpolation in fuzzy rule bases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the ternary spatial relation "Between"
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Two nonparametric models for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive noise cancellation using enhanced dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy rule interpolation for multidimensional input spaces with applications: a case study
IEEE Transactions on Fuzzy Systems
Inverse controller design for fuzzy interval systems
IEEE Transactions on Fuzzy Systems
Novel Fuzzy Inference System (FIS) Analysis and Design Based on Lattice Theory
IEEE Transactions on Fuzzy Systems
Solving Systems of Linear Fuzzy Equations by Parametric Functions
IEEE Transactions on Fuzzy Systems
Fuzzy inference based on families of α-level sets
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Evidence supporting measure of similarity for reducing the complexity in information fusion
Information Sciences: an International Journal
Lattice independent component analysis for functional magnetic resonance imaging
Information Sciences: an International Journal
Granular fuzzy inference system (FIS) design by lattice computing
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Optimal mean-square state and parameter estimation for stochastic linear systems with Poisson noises
Information Sciences: an International Journal
Rough set theory applied to lattice theory
Information Sciences: an International Journal
Information Sciences: an International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
Asynchronism-based principal component analysis for time series data mining
Expert Systems with Applications: An International Journal
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Linear models are preferable due to simplicity. Nevertheless, non-linear models often emerge in practice. A popular approach for modeling nonlinearities is by piecewise-linear approximation. Inspired from fuzzy inference systems (FISs) of Tagaki-Sugeno-Kang (TSK) type as well as from Kohonen's self-organizing map (KSOM) this work introduces a genetically optimized synergy based on intervals' numbers, or INs for short. The latter (INs) are interpreted here either probabilistically or possibilistically. The employment of mathematical lattice theory is instrumental. Advantages include accommodation of granular data, introduction of tunable nonlinearities, and induction of descriptive decision-making knowledge (rules) from the data. Both efficiency and effectiveness are demonstrated in three benchmark problems. The proposed computational method demonstrates invariably a better capacity for generalization; moreover, it learns orders-of-magnitude faster than alternative methods inducing clearly fewer rules.