TY - CHAP
T1 - NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems
AU - Todorov, Yancho
AU - Terziyska, Margarita
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049694926&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78437-3_8
DO - 10.1007/978-3-319-78437-3_8
M3 - Chapter or book article
AN - SCOPUS:85049694926
SN - 978-3-319-78436-6
T3 - Studies in Systems, Decision and Control
SP - 181
EP - 214
BT - Practical Issues of Intelligent Innovations
A2 - Sgurev, Vassil
A2 - Jotsov, Vladimir
A2 - Kacprzyk, Janusz
PB - Springer
ER -