Advances in Artificial Intelligence
Machine Intelligence Solutions
  • Home
  • Research
  • Applications
  • Projects
    • PrivacyPreservingTransferLearning
    • DeepStressAssessment
    • PrivacyPreservingDeepLearning
    • NonparametricDeepLearning
    • DeepGaussianFuzzyMappings
    • WaterApplications
    • ImageMining
    • AnalyticalFuzzyTheory
    • JobTasksAssessment
    • MembershipsOptimization
    • ImageDenoising
    • ImageDescriptors
    • ChildEarBiometrics
    • StochasticFramework
    • SFFPL
    • StochasticFuzzySystems
    • PhysiologicalSignalAnalysis
    • EnvironmentalModeling
    • QSAR
    • WorkloadScoreModeling
    • MentalStressAssessment
    • FitnessEstimation
    • FuzzyFiltering
  • Media
  • Publications
  • Services
  • Author
  • Blogs

Fuzzy Filtering

To deal with the real-world problems characterized by complexities and uncertainties, the design of the fuzzy filters is an important issue since the real-world applications require the filtering of uncertainties from the experimental data. Therefore, we developed a mathematical framework for the design and analysis of fuzzy based intelligent systems taking into account the underlying uncertainties in a sensible way. This framework facilitate the design of algorithms optimized for performance and complexity making them suitable for a real-time operation. Fuzzy filtering algorithms, i.e. the strategies for estimating the parameters of a nonlinear fuzzy filter, were developed based on different mathematical criteria. The studied mathematical criteria include

1. Robust Regularized Least-Squares Estimation
2.
H∞−optimalEstimation
3. Least-Squares Estimation
4. Generalized Least-mean-squares like
p−norm Algorithms 
5. Risk-sensitive Estimation

A mathematical theory was developed for the stability, robustness, and steady-state analyses of fuzzy filtering algorithms. 
Picture

Related Publications

  • M. Kumar, R. Stoll, and N. Stoll, “A robust design criterion for interpretable fuzzy models with uncertain data,” IEEE Transactions on Fuzzy Systems, vol. 14, pp. 314-328, Apr. 2006.
  • M. Kumar, R. Stoll, and N. Stoll, “A min-max approach to fuzzy clustering, estimation, and identification,” IEEE Transactions on Fuzzy Systems, vol. 14, pp. 248-262, Apr. 2006.
  • M. Kumar, N. Stoll, and R. Stoll, “An energy-gain bounding approach to robust fuzzy identification,” Automatica, vol. 42, pp. 711-721, May 2006.
  • M. Kumar, R. Stoll, and N. Stoll, “Deterministic approach to robust adaptive learning of fuzzy models,” IEEE Transactions System, Man, and Cybernetics, Part B: Cybernetics, vol. 36, pp. 767-780, Aug. 2006.
  • M. Kumar, N. Stoll, and R. Stoll, “Adaptive fuzzy filtering in a deterministic setting,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 4, pp. 763-776, Aug. 2009. 
  •  M. Kumar, N. Stoll, and R. Stoll, “On the estimation of parameters of takagi-sugeno fuzzy filters,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 1, pp. 150-166, Feb. 2009. 

Let us chat ...

Looking for digitalization solutions for a project?
Email us at
mohit.kumar at uni-rostock.de