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Neurocomputations in Relational Systems
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Mathematical Methods for Neural Network Analysis and Design
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A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
Fuzzy Systems Engineering: Toward Human-Centric Computing
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Fuzzy relational neural network
International Journal of Approximate Reasoning
A motion compression/reconstruction method based on max t-norm composite fuzzy relational equations
Information Sciences: an International Journal
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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OR/AND neuron in modeling fuzzy set connectives
IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Permutation-based finite implicative fuzzy associative memories
Information Sciences: an International Journal
An agent model for incremental rough set-based rule induction in customer relationship management
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Unsupervised classification of audio signals by self-organizing maps and bayesian labeling
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A social network-based approach to expert recommendation system
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Fuzzy sliding mode control with chattering elimination for a quadrotor helicopter in vertical flight
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Stroke based handwritten character recognition
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
On how percolation threshold affects PSO performance
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Supervision strategy of a solar volumetric receiver using NN and rule based techniques
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A predictive evolutionary algorithm for dynamic constrained inverse kinematics problems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Reasoning with qualitative velocity: towards a hybrid approach
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Research of neural network classifier based on FCM and PSO for breast cancer classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Engineering Applications of Artificial Intelligence
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In this study, we concentrate on the fundamentals and essential development issues of logic-driven constructs of fuzzy neural networks. These networks, referred to as logic-oriented neural networks, constitute an interesting conceptual and computational framework that greatly benefits from the establishment of highly synergistic links between the technology of fuzzy sets (or granular computing, being more general) and neural networks. The most essential advantages of the proposed networks are twofold. First, the transparency of neural architectures becomes highly relevant when dealing with the mechanisms of efficient learning. Here the learning is augmented by the fact that domain knowledge could be easily incorporated in advance prior to any learning. This becomes possible given the compatibility between the architecture of the problem and the induced topology of the neural network. Second, once the training has been completed, the network can be easily interpreted and thus it directly translates into a series of truth-quantifiable logic expressions formed over a collection of information granules. The design process of the logic networks synergistically exploits the principles of information granulation, logic computing and underlying optimization including those biologically inspired techniques (such as particle swarm optimization, genetic algorithms and alike). We elaborate on the existing development trends, present key methodological pursuits and algorithms. In particular, we show how the logic blueprint of the networks is supported by the use of various constructs of fuzzy sets including logic operators, logic neurons, referential operators and fuzzy relational constructs.