000 03554nam a22004935i 4500
001 978-3-319-02135-5
003 DE-He213
005 20200421112219.0
007 cr nn 008mamaa
008 131128s2014 gw | s |||| 0|eng d
020 _a9783319021355
_9978-3-319-02135-5
024 7 _a10.1007/978-3-319-02135-5
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aSiddique, Nazmul.
_eauthor.
245 1 0 _aIntelligent Control
_h[electronic resource] :
_bA Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms /
_cby Nazmul Siddique.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXVII, 282 p. 158 illus., 55 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v517
505 0 _aIntroduction -- Dynamical Systems -- Control Systems -- Mathematics of Fuzzy Control -- Fuzzy Control -- GA-Fuzzy Control -- Neuro-Fuzzy Control -- GA-Neuro-Fuzzy Control -- Stability Analysis -- Epilogue and Future Work.
520 _aIntelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of the fuzzy controller is then described and finally an evolutionary algorithm is applied to the neurally-tuned-fuzzy controller in which the sigmoidal function shape of the neural network is determined. The important issue of stability is addressed and the text demonstrates empirically that the developed controller was stable within the operating range. The text concludes with ideas for future research to show the reader the potential for further study in this area. Intelligent Control will be of interest to researchers from engineering and computer science backgrounds working in the intelligent and adaptive control.
650 0 _aEngineering.
650 0 _aComputer simulation.
650 0 _aSystem theory.
650 0 _aControl engineering.
650 1 4 _aEngineering.
650 2 4 _aControl.
650 2 4 _aSystems Theory, Control.
650 2 4 _aSimulation and Modeling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319021348
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v517
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02135-5
912 _aZDB-2-ENG
942 _cEBK
999 _c57283
_d57283