LEADER 01101nam a2200301 i 4500 001 991003675429707536 005 20021220115233.0 008 020916s2001 us ||| | eng 020 $a1584882344 035 $ab11846744-39ule_inst 035 $aLE01313170$9ExL 040 $aDip.to Matematica$beng 082 0 $a519.23 084 $aAMS 60H 100 1 $aMeyer, Michael$0352338 245 10$aContinuous stochastic calculus with applications to finance /$cMichael Meyer 260 $aBoca Raton (USA) :$bChapman & Hall/CRC,$cc2001 300 $axvi, 319 p. ;$c24 cm 490 0 $aApplied mathematics ;$v17 500 $aIncludes bibliographical references 650 0$aFinance-Mathematical models 650 0$aStochastic analysis 907 $a.b11846744$b28-04-17$c20-12-02 912 $a991003675429707536 945 $aLE013 60H MEY11 (2001)$g1$i2013000135274$lle013$o-$pE0.00$q-$rl$s- $t0$u3$v0$w3$x0$y.i12099089$z20-12-02 996 $aContinuous stochastic calculus with applications to finance$9900109 997 $aUNISALENTO 998 $ale013$b01-01-02$cm$da $e-$feng$gus $h0$i1 LEADER 04686nam 22006135 450 001 9910766880403321 005 20231128070845.0 010 $a3-031-47835-5 024 7 $a10.1007/978-3-031-47835-2 035 $a(CKB)29092386700041 035 $a(MiAaPQ)EBC30977747 035 $a(Au-PeEL)EBL30977747 035 $a(DE-He213)978-3-031-47835-2 035 $a(EXLCZ)9929092386700041 100 $a20231128d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSynchronization Control of Markovian Complex Neural Networks with Time-varying Delays /$fby Junyi Wang, Jun Fu 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (162 pages) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v514 311 08$a9783031478345 320 $aIncludes bibliographical references. 327 $aIntroduction -- Stochastic synchronization of Markovian coupled neural networks with partially unknown transition rates -- Local synchronization of Markovian neutral-type complex networks with partially unknown transition rates -- Local Synchronization of Markovian nonlinearly coupled neural networks with generally uncertain transition rates -- Sampled-data synchronization of complex networks based on discontinuous Lyapunov-Krasovskii functional -- Sampled-data synchronization of Markovian coupled neural networks with time-varying mode delays -- Synchronization criteria of delayed inertial neural networks with generally uncertain transition rates -- Conclusions. 330 $aThis monograph studies the synchronization control of Markovian complex neural networks with time-varying delays, and the structure of the book is summarized as follows. Chapter 1 introduces the system description and some background knowledges, and also addresses the motivations of this monograph. In Chapter 2, the stochastic synchronization issue of Markovian coupled neural networks with partially unknown transition rates and random coupling strengths is investigated. In Chapter 3, the local synchronization issue of Markovian neutral complex networks with partially information of transition rates is investigated. The new delay-dependent synchronization criteria in terms of LMIs are derived, which depends on the upper and lower bounds of the delays. In Chapter 4, the local synchronization issue of Markovian nonlinear coupled neural networks with uncertain and partially unknown transition rates is investigated. The less conservative local synchronization criteria containing the bounds of delay and delay derivative are obtained based on the novel augmented Lyapunov-Krasovskii functional and a new integral inequality. In Chapter 5, the sampled-data synchronization issue of delayed complex networks with aperiodic sampling interval is investigated based on enhanced input delay approach, which makes full use of the upper bound of the variable sampling interval and the sawtooth structure information of varying input delay. In Chapter 6, the sampled-data synchronization issue of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals is investigated based on an enhanced input delay approach. Furthermore, the mode-dependent sampled-data controllers are proposed based on the delay dependent synchronization criteria. In Chapter 7, the synchronization issue of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. In Chapter 8, we conclude the monograph by briefly summarizing the main theoretical findings. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v514 606 $aElectrical engineering 606 $aAutomatic control 606 $aRobotics 606 $aAutomation 606 $aElectrical and Electronic Engineering 606 $aControl, Robotics, Automation 606 $aControl and Systems Theory 615 0$aElectrical engineering. 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aAutomation. 615 14$aElectrical and Electronic Engineering. 615 24$aControl, Robotics, Automation. 615 24$aControl and Systems Theory. 676 $a006.32 700 $aWang$b Junyi$f1937-$01460123 702 $aFu$b Jun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910766880403321 996 $aSynchronization Control of Markovian Complex Neural Networks with Time-Varying Delays$93659809 997 $aUNINA