Using neural networks to determine Sugeno measures by statistics
Neural Networks
A genetic algorithm for determining nonadditive set functions in information fusion
Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
A new type of nonlinear integrals and the computational algorithm
Fuzzy Sets and Systems
Optimization issues for fuzzy measures
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - A special issue on fuzzy measures
Machine Learning
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Fuzzy Measure Theory
A selection approach for scalable fuzzy integral combination
Information Fusion
Engineering Applications of Artificial Intelligence
An Empirical Study of Statistical Properties of the Choquet and Sugeno Integrals
IEEE Transactions on Fuzzy Systems
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
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An important limitation of fuzzy integrals for information fusion is the exponential growth of coefficients for an increasing number of information sources. To overcome this problem a variety of fuzzy measure identification algorithms has been proposed. HLMS is a simple gradient-based algorithm for fuzzy measure identification which suffers from some convergence problems. In this paper, two proposals for HLMS convergence improvement are presented, a modified formula for coefficients update and new policy for monotonicity check. A comprehensive experimental work shows that these proposals indeed contribute to HLMS convergence, accuracy and robustness.