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NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems

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Practical Issues of Intelligent Innovations

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 140))

Abstract

Capturing the dynamics and control of fast complex nonlinear systems often requires the application of computationally efficient modeling structures in order to track the system behavior without loss of accuracy and to provide reliable predictions on purpose to process control. An available approach is to employ fuzzy-neural networks, whose abilities to handle dynamical data streams and to build rule-based relationships makes them a flexible solution. A major drawback of the classical fuzzy-neural networks is the large number of parameters associated with the rules premises and consequents parts, which need to be adapted at each discrete time instant. Therefore, in this chapter several structures with reduced number of parameters lying in the framework of a NEO-Fuzzy neuron are proposed. To increase the robustness of the models when addressing to uncommon/uncertain data variations, Type-2 and Intuitionistic fuzzy logic are introduced. An approach to design a simple NEO-Fuzzy state-space predictive controller shows the potential applicability of the proposed models for process control.

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Author information

Authors and Affiliations

  1. Department of Chemical and Metallurgical Engineering, Lab. of Automation and Process Control, Aalto University, Kemistintie 1, 02100, Espoo, Finland

    Yancho Todorov

  2. Department of Informatics and Statistics, University of Food Technologies, 26 Maritza blvd., Plovdiv, Bulgaria

    Margarita Terziyska

Authors
  1. Yancho Todorov
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  2. Margarita Terziyska
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Correspondence to Yancho Todorov .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev

  2. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Vladimir Jotsov

  3. Department of Computer Science, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Todorov, Y., Terziyska, M. (2018). NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Practical Issues of Intelligent Innovations. Studies in Systems, Decision and Control, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-78437-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-78437-3_8

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