Foundations of logic programming
Foundations of logic programming
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
Monotonic and Residuated Logic Programs
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Algorithms of Nondifferentiable Optimization: Development and Application
Cybernetics and Systems Analysis
Nonsmooth training of fuzzy neural networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The core method: connectionist model generation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Adaptation of weighted fuzzy programs
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Neural fuzzy logic programming
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Fuzzy logic programs are a useful framework for imperfect knowledge representation and reasoning using the formalism of logic programming. Nevertheless, there is the need for modeling adaptation of fuzzy logic programs, so that machine learning techniques, such as connectionist-based learning, can be applied. Weighted fuzzy logic programs bring fuzzy logic programs and connectionist models closer together by associating a significant weight with each atom in the body of a fuzzy rule: by exploiting the existence of the weights, it is possible to construct a connectionist model that reflects the exact structure of a weighted fuzzy logic program. Based on the connectionist representation, we first define the weight adaptation problem as the task of adapting the weights of the rules of a weighted fuzzy logic program, so that they fit best a set of training data, and then we develop a subgradient descent learning algorithm for the connectionist model that allows us to obtain an approximate solution for the weight adaptation problem.