LEADER 01298aam 2200385I 450 001 9910709532703321 005 20151026025816.0 024 8 $aGOVPUB-C13-cd49d4c7a97cb34dcaf16e9fb7e3dc14 035 $a(CKB)5470000002479676 035 $a(OCoLC)926740463 035 $a(EXLCZ)995470000002479676 100 $a20151026d1985 ua 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aStandard reference materials $ehandbook for SRM users/$fJohn K. Taylor 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d1985. 215 $a1 online resource 225 1 $aNBS special publication ;$v260-100 300 $a1985. 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from PDF title page. 320 $aIncludes bibliographical references. 517 $aStandard reference materials 700 $aTaylor$b John K$095171 701 $aTaylor$b John K$095171 712 02$aUnited States.$bNational Bureau of Standards. 801 0$bNBS 801 1$bNBS 801 2$bGPO 906 $aBOOK 912 $a9910709532703321 996 $aStandard reference materials$93512378 997 $aUNINA LEADER 03781nam 2200577 a 450 001 9910739479503321 005 20241003104329.0 010 $a1-299-19715-9 010 $a1-4471-4914-9 024 7 $a10.1007/978-1-4471-4914-9 035 $a(PPN)168294508 035 $a(OCoLC)828734466 035 $a(MiFhGG)GVRL6UYX 035 $a(CKB)2670000000530162 035 $a(MiAaPQ)EBC1106245 035 $a(EXLCZ)992670000000530162 100 $a20130102d2013 uy 0 101 0 $aeng 135 $aurun#---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aNonlinear stochastic systems with incomplete information $efiltering and control /$fBo Shen, Zidong Wang, Huisheng Shu 205 $a1st ed. 2013. 210 $aLondon $cSpringer$d2013 215 $a1 online resource (xvi, 248 pages) $cillustrations (some color) 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a1-4471-4913-0 311 $a1-4471-6000-2 320 $aIncludes bibliographical references and index. 327 $aFrom the Contents: Quantized H-infinity Control for Nonlinear Stochastic Time-delay Systems with Missing Measurements -- Nonlinear H-infinity Filtering for Discrete-Time Stochastic Systems with Missing Measurements and Randomly Varying Sensor Delays -- Robust H-infinity Filtering with Randomly Occurring Nonlinearities, Quantization Effects and Successive Packet Dropouts -- H-infinity Filtering with Randomly Occurring Sensor Saturations and Missing Measurements -- Distributed H-infinity Consensus Filtering in Sensor Networks with Multiple Missing Measurements: The Finite-Horizon Case. 330 $aNonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H? filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: ·         a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; ·         new concepts such as random sensor and signal saturations for more realistic modeling; and ·         demonstration of the use of techniques such as the Hamilton?Jacobi?Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book. 606 $aStochastic systems 606 $aNonlinear theories 615 0$aStochastic systems. 615 0$aNonlinear theories. 676 $a519 676 $a519.2 676 $a519.22 676 $a530.1/5 700 $aShen$b Bo$01424201 701 $aWang$b Zidong$0720602 701 $aShu$b Huisheng$01755718 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739479503321 996 $aNonlinear stochastic systems with incomplete information$94192621 997 $aUNINA LEADER 03993nam 2201021z- 450 001 9910346856603321 005 20210211 010 $a3-03897-665-2 035 $a(CKB)4920000000095101 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/50225 035 $a(oapen)doab50225 035 $a(EXLCZ)994920000000095101 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInformation Theory in Neuroscience 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 online resource (280 p.) 311 08$a3-03897-664-4 330 $aAs the ultimate information processing device, the brain naturally lends itself to being studied with information theory. The application of information theory to neuroscience has spurred the development of principled theories of brain function, and has led to advances in the study of consciousness, as well as to the development of analytical techniques to crack the neural code-that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience. 610 $abrain network 610 $acategorical perception 610 $achannel capacity 610 $acomplex networks 610 $aconnectome 610 $aconsciousness 610 $adecoding 610 $adiscrete Markov chains 610 $adiscrimination 610 $aeigenvector centrality 610 $aentorhinal cortex 610 $afeedforward networks 610 $afree-energy principle 610 $afunctional connectome 610 $aGibbs measures 610 $agoodness 610 $agraph theoretical analysis 610 $agraph theory 610 $ahigher-order correlations 610 $ahippocampus 610 $aindependent component analysis 610 $ainfomax principle 610 $ainformation entropy production 610 $ainformation theory 610 $aintegrated information 610 $aintegrated information theory 610 $ainternal model hypothesis 610 $aIsing model 610 $alatching 610 $amaximum entropy 610 $amaximum entropy principle 610 $aminimum information partition 610 $amismatched decoding 610 $amutual information 610 $amutual information decomposition 610 $anavigation 610 $anetwork eigen-entropy 610 $aneural code 610 $aneural coding 610 $aneural information propagation 610 $aneural network 610 $aneural population coding 610 $aneuroscience 610 $anoise correlations 610 $aorderness 610 $aperceived similarity 610 $aperceptual magnet 610 $aPotts model 610 $aprincipal component analysis 610 $apulse-gating 610 $aQueyranne's algorithm 610 $arecursion 610 $aredundancy 610 $arepresentation 610 $aspike train statistics 610 $aspike-time precision 610 $asubmodularity 610 $asynergy 610 $aunconscious inference 700 $aPiasini$b Eugenio$4auth$01292375 702 $aPanzeri$b Stefano$4auth 906 $aBOOK 912 $a9910346856603321 996 $aInformation Theory in Neuroscience$93022229 997 $aUNINA