| RESEARCH | RESEARCH PROJECTS |

Soft computing methods and techniques
  • Artificial neural networks: structure optimization of multi-layer feedforward networks; GMDH (Group Method and Data Hadling) networks and their extentions; networks with the dynamic model of neurons; neural network ensembles; training algorithms: gradient and genetic
  • fuzzy and neuro-fuzzy systems: structure and parameter optimization via evolutionary and gradient algorithms; bounded-error analysis
  • expert systems: integrated knowledge bases, knowledge representation (rules, analytical, neural), knowledge aquisition, fuzzy logic; applications to air pollution processes and power plants diagnosis systems

Fault detection and isolation (FDI)
  • Analytical methods: model-based approaches, robust observers (Kalman and Luenberger filters), unknown input observers (bounded-error technique, mathematical model design via genetic programming)
  • Soft computing methods: neural and neuro-fuzzy models and classifiers, optimization of diagnostic systems via evolutionary algorithms, expert systems, integration of qualitative and quantitative knowledge

Modelling and simulation
  • Distributed parameter systems: modelling, state and parameter estimation, Kalman filters, sensors and actuators location (stationary and moving)
  • Applications: air pollution processes on the city scale; nuclear power reactors, industrial processes -- sugar and power plants

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