Information and self-organization: a macroscopic approach to complex systems
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2006
|
| Ausgabe: | 3., enl. ed. |
| Schriftenreihe: | Springer series in synergetics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 1. ed. appeared as vol. 40 |
| Beschreibung: | XIV, 257 S. Ill., graph. Darst. 24 cm |
| ISBN: | 3540330216 9783540330219 |
Internformat
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Datensatz im Suchindex
| _version_ | 1807953578912907264 |
|---|---|
| adam_text |
HERMANN HAKEN INFORMATION AND SELF-ORGANIZATION A MACROSCOPIC APPROACH
TO COMPLEX SYSTEMS THIRD ENLARGED EDITION WITH 66 FIGURES 4Y SPRINGER
CONTENTS 1. THE CHALLENGE OF COMPLEX SYSTEMS 1 1.1 WHAT ARE COMPLEX
SYSTEMS? 1 1.2 HOW TO DEAL WITH COMPLEX SYSTEMS 5 1.3 MODEL SYSTEMS 7
1.4 SELF-ORGANIZATION 10 1.5 AIMING AT UNIVERSALITY 11 1.5.1
THERMODYNAMICS 11 1.5.2 STATISTICAL PHYSICS 12 1.5.3 SYNERGETICS 13 1.6
INFORMATION 14 1.6.1 SHANNON INFORMATION: MEANING EXORCISED 15 1.6.2
EFFECTS OF INFORMATION 16 1.6.3 SELF-CREATION OF MEANING 23 1.6.4 HOW
MUCH INFORMATION DO WE NEED TO MAINTAIN AN ORDERED STATE? 29 1.7 THE
SECOND FOUNDATION OF SYNERGETICS 33 2. FROM THE MICROSCOPIC TO THE
MACROSCOPIC WORLD 36 2.1 LEVELS OF DESCRIPTION 36 2.2 LANGEVIN EQUATIONS
37 2.3 FOKKER-PLANCK EQUATION 40 2.4 EXACT STATIONARY SOLUTION OF THE
FOKKER-PLANCK EQUATION FOR SYSTEMS IN DETAILED BALANCE 41 2.4.1 DETAILED
BALANCE 41 2.4.2 THE REQUIRED STRUCTURE OF THE FOKKER-PLANCK EQUATION
AND ITS STATIONARY SOLUTION 42 2.5 PATH INTEGRALS 44 2.6 REDUCTION OF
COMPLEXITY, ORDER PARAMETERS AND THE SLAVING PRINCIPLE 45 2.6.1 LINEAR
STABILITY ANALYSIS 46 2.6.2 TRANSFORMATION OF EVOLUTION EQUATIONS 47
2.6.3 THE SLAVING PRINCIPLE 48 2.7 NONEQUILIBRIUM PHASE TRANSITIONS 49
2.8 PATTERN FORMATION 51 3. . AND BACK AGAIN: THE MAXIMUM INFORMATION
PRINCIPLE (MIP) . 53 3.1 SOME BASIC IDEAS 53 3.2 INFORMATION GAIN 57
XII CONTENTS 3.3 INFORMATION ENTROPY AND CONSTRAINTS 58 3.4 CONTINUOUS
VARIABLES 63 4. AN EXAMPLE FROM PHYSICS: THERMODYNAMICS 65 5.
APPLICATION OF THE MAXIMUM INFORMATION PRINCIPLE TO SELF-ORGANIZING
SYSTEMS 69 5.1 INTRODUCTION 69 5.2 APPLICATION TO SELF-ORGANIZING
SYSTEMS: SINGLE MODE LASER 69 5.3 MULTIMODE LASER WITHOUT PHASE
RELATIONS 71 5.4 PROCESSES PERIODIC IN ORDER PARAMETERS 72 6. THE
MAXIMUM INFORMATION PRINCIPLE FOR NONEQUILIBRIUM PHASE TRANSITIONS:
DETERMINATION OF ORDER PARAMETERS, ENSLAVED MODES, AND EMERGING PATTERNS
74 6.1 INTRODUCTION 74 6.2 GENERAL APPROACH 74 6.3 DETERMINATION OF
ORDER PARAMETERS, ENSLAVED MODES, AND EMERGING PATTERNS 76 6.4
APPROXIMATIONS 77 6.5 SPATIAL PATTERNS 78 6.6 RELATION TO THE LANDAU
THEORY OF PHASE TRANSITIONS. GUESSING OF FOKKER-PLANCK EQUATIONS 79 7.
INFORMATION, INFORMATION GAIN, AND EFFICIENCY OF SELF-ORGANIZING SYSTEMS
CLOSE TO THEIR INSTABILITY POINTS 8 1 7.1 INTRODUCTION 81 7.2 THE
SLAVING PRINCIPLE AND ITS APPLICATION TO INFORMATION 82 7.3 INFORMATION
GAIN 82 7.4 AN EXAMPLE: NONEQUILIBRIUM PHASE TRANSITIONS 83 7.5 SOFT
SINGLE-MODE INSTABILITIES 84 7.6 CAN WE MEASURE THE INFORMATION AND THE
INFORMATION GAIN? . 85 7.6.1 EFFICIENCY 85 7.6.2 INFORMATION AND
INFORMATION GAIN 86 7.7 SEVERAL ORDER PARAMETERS 87 7.8 EXPLICIT
CALCULATION OF THE INFORMATION OF A SINGLE ORDER PARAMETER 88 7.8.1 THE
REGION WELL BELOW THRESHOLD 89 7.8.2 THE REGION WELL ABOVE THRESHOLD 90
7.8.3 NUMERICAL RESULTS 93 7.8.4 DISCUSSION 94 7.9 EXACT ANALYTICAL
RESULTS ON INFORMATION, INFORMATION GAIN, AND EFFICIENCY OF A SINGLE
ORDER PARAMETER 95 7.9.1 THE INSTABILITY POINT 97 7.9.2 THE APPROACH TO
INSTABILITY 98 7.9.3 THE STABLE REGION 99 7.9.4 THE INJECTED SIGNAL 100
CONTENTS XM 7.9.5 CONCLUSIONS 101 7.10 THE 5-THEOREM OF KLIMONTOVICH 102
7.10.1 REGION 1: BELOW LASER THRESHOLD 104 7.10.2 REGION 2: AT THRESHOLD
104 7.10.3 REGION 3: WELL ABOVE THRESHOLD 105 7.11 THE CONTRIBUTION OF
THE ENSLAVED MODES TO THE INFORMATION CLOSE TO NONEQUILIBRIUM PHASE
TRANSITIONS 107 8. DIRECT DETERMINATION OF LAGRANGE MULTIPLIERS 115 8.1
INFORMATION ENTROPY OF SYSTEMS BELOW AND ABOVE THEIR CRITICAL POINT 115
8.2 DIRECT DETERMINATION OF LAGRANGE MULTIPLIERS BELOW, AT AND ABOVE THE
CRITICAL POINT 117 9. UNBIASED MODELING OF STOCHASTIC PROCESSES: HOW TO
GUESS PATH INTEGRALS, FOKKER-PLANCK EQUATIONS AND LANGEVIN-ITO EQUATIONS
. 125 9.1 ONE-DIMENSIONAL STATE VECTOR 125 9.2 GENERALIZATION TO A
MULTIDIMENSIONAL STATE VECTOR 127 9.3 CORRELATION FUNCTIONS AS
CONSTRAINTS 130 9.4 THE FOKKER-PLANCK EQUATION BELONGING TO THE
SHORT-TIME PROPAGATOR 132 9.5 CAN WE DERIVE NEWTON'S LAW FROM
EXPERIMENTAL DATA? 133 10. APPLICATION TO SOME PHYSICAL SYSTEMS 135 10.1
MULTIMODE LASERS WITH PHASE RELATIONS 135 10.2 THE SINGLE-MODE LASER
INCLUDING POLARIZATION AND INVERSION . 136 10.3 FLUID DYNAMICS: THE
CONVECTION INSTABILITY 138 11. TRANSITIONS BETWEEN BEHAVIORAL PATTERNS
IN BIOLOGY, AN EXAMPLE: HAND MOVEMENTS 140 11.1 SOME EXPERIMENTAL FACTS
140 11.2 HOW TO MODEL THE TRANSITION 141 11.3 CRITICAL FLUCTUATIONS 147
11.4 SOME CONCLUSIONS 151 12. PATTERN RECOGNITION. UNBIASED GUESSES OF
PROCESSES: EXPLICIT DETERMINATION OF LAGRANGE MULTIPLIERS 153 12.1
FEATURE SELECTION 153 12.2 AN ALGORITHM FOR PATTERN RECOGNITION 159 12.3
THE BASIC CONSTRUCTION PRINCIPLE OF A SYNERGETIC COMPUTER 161 12.4
LEARNING BY MEANS OF THE INFORMATION GAIN 163 12.5 PROCESSES AND
ASSOCIATIVE ACTION 165 12.6 EXPLICIT DETERMINATION OF THE LAGRANGE
MULTIPLIERS OF THE CONDITIONAL PROBABILITY. GENERAL APPROACH FOR
DISCRETE AND CONTINUOUS PROCESSES 169 12.7 APPROXIMATION AND SMOOTHING
SCHEMES. ADDITIVE NOISE 174 XIV CONTENTS 12.8 AN EXPLICIT EXAMPLE:
BROWNIAN MOTION 181 12.9 APPROXIMATION AND SMOOTHING SCHEMES.
MULTIPLICATIVE (AND ADDITIVE) NOISE 184 12.10 EXPLICIT CALCULATION OF
DRIFT AND DIFFUSION COEFFICIENTS. EXAMPLES 185 12.11 PROCESS MODELLING,
PREDICTION AND CONTROL, ROBOTICS 187 12.12 NON-MARKOVIAN PROCESSES.
CONNECTION WITH CHAOS THEORY 189 12.12.1 CHECKING THE MARKOV PROPERTY
189 12.12.2 TIME SERIES ANALYSIS 190 13. INFORMATION COMPRESSION IN
COGNITION: THE INTERPLAY BETWEEN SHANNON AND SEMANTIC INFORMATION 195
13.1 INFORMATION COMPRESSION: A GENERAL FORMULA 195 13.2 PATTERN
RECOGNITION AS INFORMATION COMPRESSION: USE OF SYMMETRIES 197 13.3
DEFORMATIONS 199 13.4 REINTERPRETATION OF THE RESULTS OF SECTS.
13.1-13.3 201 14. QUANTUM SYSTEMS 203 14.1 WHY QUANTUM THEORY OF
INFORMATION? 203 14.2 THE MAXIMUM INFORMATION PRINCIPLE 205 14.3 ORDER
PARAMETERS, ENSLAVED MODES AND PATTERNS 211 14.4 INFORMATION OF ORDER
PARAMETERS AND ENSLAVED MODES 214 15. QUANTUM INFORMATION 216 15.1 BASIC
CONCEPTS OF QUANTUM INFORMATION. Q-BITS 216 15.2 PHASE AND DECOHERENCE
218 15.3 REPRESENTATION OF NUMBERS 219 15.4 REGISTER 220 15.5
ENTANGLEMENT 221 16. QUANTUM COMPUTATION 222 16.1 CLASSICAL GATES 222
16.2 QUANTUM GATES 223 16.3 CALCULATION OF THE PERIOD OF A SEQUENCE BY A
QUANTUM COMPUTER . 227 16.4 CODING, DECODING AND BREAKING CODES 229
16.4.1 A LITTLE MATHEMATICS 230 16.4.2 RSA CODING AND DECODING 230
16.4.3 SHOR'S APPROACH, CONTINUED 231 16.5 THE PHYSICS OF SPIN 1/2 233
16.6 QUANTUM THEORY OF A SPIN IN MUTUALLY PERPENDICULAR MAGNETIC FIELDS,
ONE CONSTANT AND ONE TIME DEPENDENT 235 16.7 QUANTUM COMPUTATION AND
SELF-ORGANIZATION 241 17. CONCLUDING REMARKS AND OUTLOOK 242 REFERENCES
244 SUBJECT INDEX 25 1 |
| adam_txt |
HERMANN HAKEN INFORMATION AND SELF-ORGANIZATION A MACROSCOPIC APPROACH
TO COMPLEX SYSTEMS THIRD ENLARGED EDITION WITH 66 FIGURES 4Y SPRINGER
CONTENTS 1. THE CHALLENGE OF COMPLEX SYSTEMS 1 1.1 WHAT ARE COMPLEX
SYSTEMS? 1 1.2 HOW TO DEAL WITH COMPLEX SYSTEMS 5 1.3 MODEL SYSTEMS 7
1.4 SELF-ORGANIZATION 10 1.5 AIMING AT UNIVERSALITY 11 1.5.1
THERMODYNAMICS 11 1.5.2 STATISTICAL PHYSICS 12 1.5.3 SYNERGETICS 13 1.6
INFORMATION 14 1.6.1 SHANNON INFORMATION: MEANING EXORCISED 15 1.6.2
EFFECTS OF INFORMATION 16 1.6.3 SELF-CREATION OF MEANING 23 1.6.4 HOW
MUCH INFORMATION DO WE NEED TO MAINTAIN AN ORDERED STATE? 29 1.7 THE
SECOND FOUNDATION OF SYNERGETICS 33 2. FROM THE MICROSCOPIC TO THE
MACROSCOPIC WORLD 36 2.1 LEVELS OF DESCRIPTION 36 2.2 LANGEVIN EQUATIONS
37 2.3 FOKKER-PLANCK EQUATION 40 2.4 EXACT STATIONARY SOLUTION OF THE
FOKKER-PLANCK EQUATION FOR SYSTEMS IN DETAILED BALANCE 41 2.4.1 DETAILED
BALANCE 41 2.4.2 THE REQUIRED STRUCTURE OF THE FOKKER-PLANCK EQUATION
AND ITS STATIONARY SOLUTION 42 2.5 PATH INTEGRALS 44 2.6 REDUCTION OF
COMPLEXITY, ORDER PARAMETERS AND THE SLAVING PRINCIPLE 45 2.6.1 LINEAR
STABILITY ANALYSIS 46 2.6.2 TRANSFORMATION OF EVOLUTION EQUATIONS 47
2.6.3 THE SLAVING PRINCIPLE 48 2.7 NONEQUILIBRIUM PHASE TRANSITIONS 49
2.8 PATTERN FORMATION 51 3. . AND BACK AGAIN: THE MAXIMUM INFORMATION
PRINCIPLE (MIP) . 53 3.1 SOME BASIC IDEAS 53 3.2 INFORMATION GAIN 57
XII CONTENTS 3.3 INFORMATION ENTROPY AND CONSTRAINTS 58 3.4 CONTINUOUS
VARIABLES 63 4. AN EXAMPLE FROM PHYSICS: THERMODYNAMICS 65 5.
APPLICATION OF THE MAXIMUM INFORMATION PRINCIPLE TO SELF-ORGANIZING
SYSTEMS 69 5.1 INTRODUCTION 69 5.2 APPLICATION TO SELF-ORGANIZING
SYSTEMS: SINGLE MODE LASER 69 5.3 MULTIMODE LASER WITHOUT PHASE
RELATIONS 71 5.4 PROCESSES PERIODIC IN ORDER PARAMETERS 72 6. THE
MAXIMUM INFORMATION PRINCIPLE FOR NONEQUILIBRIUM PHASE TRANSITIONS:
DETERMINATION OF ORDER PARAMETERS, ENSLAVED MODES, AND EMERGING PATTERNS
74 6.1 INTRODUCTION 74 6.2 GENERAL APPROACH 74 6.3 DETERMINATION OF
ORDER PARAMETERS, ENSLAVED MODES, AND EMERGING PATTERNS 76 6.4
APPROXIMATIONS 77 6.5 SPATIAL PATTERNS 78 6.6 RELATION TO THE LANDAU
THEORY OF PHASE TRANSITIONS. GUESSING OF FOKKER-PLANCK EQUATIONS 79 7.
INFORMATION, INFORMATION GAIN, AND EFFICIENCY OF SELF-ORGANIZING SYSTEMS
CLOSE TO THEIR INSTABILITY POINTS 8 1 7.1 INTRODUCTION 81 7.2 THE
SLAVING PRINCIPLE AND ITS APPLICATION TO INFORMATION 82 7.3 INFORMATION
GAIN 82 7.4 AN EXAMPLE: NONEQUILIBRIUM PHASE TRANSITIONS 83 7.5 SOFT
SINGLE-MODE INSTABILITIES 84 7.6 CAN WE MEASURE THE INFORMATION AND THE
INFORMATION GAIN? . 85 7.6.1 EFFICIENCY 85 7.6.2 INFORMATION AND
INFORMATION GAIN 86 7.7 SEVERAL ORDER PARAMETERS 87 7.8 EXPLICIT
CALCULATION OF THE INFORMATION OF A SINGLE ORDER PARAMETER 88 7.8.1 THE
REGION WELL BELOW THRESHOLD 89 7.8.2 THE REGION WELL ABOVE THRESHOLD 90
7.8.3 NUMERICAL RESULTS 93 7.8.4 DISCUSSION 94 7.9 EXACT ANALYTICAL
RESULTS ON INFORMATION, INFORMATION GAIN, AND EFFICIENCY OF A SINGLE
ORDER PARAMETER 95 7.9.1 THE INSTABILITY POINT 97 7.9.2 THE APPROACH TO
INSTABILITY 98 7.9.3 THE STABLE REGION 99 7.9.4 THE INJECTED SIGNAL 100
CONTENTS XM 7.9.5 CONCLUSIONS 101 7.10 THE 5-THEOREM OF KLIMONTOVICH 102
7.10.1 REGION 1: BELOW LASER THRESHOLD 104 7.10.2 REGION 2: AT THRESHOLD
104 7.10.3 REGION 3: WELL ABOVE THRESHOLD 105 7.11 THE CONTRIBUTION OF
THE ENSLAVED MODES TO THE INFORMATION CLOSE TO NONEQUILIBRIUM PHASE
TRANSITIONS 107 8. DIRECT DETERMINATION OF LAGRANGE MULTIPLIERS 115 8.1
INFORMATION ENTROPY OF SYSTEMS BELOW AND ABOVE THEIR CRITICAL POINT 115
8.2 DIRECT DETERMINATION OF LAGRANGE MULTIPLIERS BELOW, AT AND ABOVE THE
CRITICAL POINT 117 9. UNBIASED MODELING OF STOCHASTIC PROCESSES: HOW TO
GUESS PATH INTEGRALS, FOKKER-PLANCK EQUATIONS AND LANGEVIN-ITO EQUATIONS
. 125 9.1 ONE-DIMENSIONAL STATE VECTOR 125 9.2 GENERALIZATION TO A
MULTIDIMENSIONAL STATE VECTOR 127 9.3 CORRELATION FUNCTIONS AS
CONSTRAINTS 130 9.4 THE FOKKER-PLANCK EQUATION BELONGING TO THE
SHORT-TIME PROPAGATOR 132 9.5 CAN WE DERIVE NEWTON'S LAW FROM
EXPERIMENTAL DATA? 133 10. APPLICATION TO SOME PHYSICAL SYSTEMS 135 10.1
MULTIMODE LASERS WITH PHASE RELATIONS 135 10.2 THE SINGLE-MODE LASER
INCLUDING POLARIZATION AND INVERSION . 136 10.3 FLUID DYNAMICS: THE
CONVECTION INSTABILITY 138 11. TRANSITIONS BETWEEN BEHAVIORAL PATTERNS
IN BIOLOGY, AN EXAMPLE: HAND MOVEMENTS 140 11.1 SOME EXPERIMENTAL FACTS
140 11.2 HOW TO MODEL THE TRANSITION 141 11.3 CRITICAL FLUCTUATIONS 147
11.4 SOME CONCLUSIONS 151 12. PATTERN RECOGNITION. UNBIASED GUESSES OF
PROCESSES: EXPLICIT DETERMINATION OF LAGRANGE MULTIPLIERS 153 12.1
FEATURE SELECTION 153 12.2 AN ALGORITHM FOR PATTERN RECOGNITION 159 12.3
THE BASIC CONSTRUCTION PRINCIPLE OF A SYNERGETIC COMPUTER 161 12.4
LEARNING BY MEANS OF THE INFORMATION GAIN 163 12.5 PROCESSES AND
ASSOCIATIVE ACTION 165 12.6 EXPLICIT DETERMINATION OF THE LAGRANGE
MULTIPLIERS OF THE CONDITIONAL PROBABILITY. GENERAL APPROACH FOR
DISCRETE AND CONTINUOUS PROCESSES 169 12.7 APPROXIMATION AND SMOOTHING
SCHEMES. ADDITIVE NOISE 174 XIV CONTENTS 12.8 AN EXPLICIT EXAMPLE:
BROWNIAN MOTION 181 12.9 APPROXIMATION AND SMOOTHING SCHEMES.
MULTIPLICATIVE (AND ADDITIVE) NOISE 184 12.10 EXPLICIT CALCULATION OF
DRIFT AND DIFFUSION COEFFICIENTS. EXAMPLES 185 12.11 PROCESS MODELLING,
PREDICTION AND CONTROL, ROBOTICS 187 12.12 NON-MARKOVIAN PROCESSES.
CONNECTION WITH CHAOS THEORY 189 12.12.1 CHECKING THE MARKOV PROPERTY
189 12.12.2 TIME SERIES ANALYSIS 190 13. INFORMATION COMPRESSION IN
COGNITION: THE INTERPLAY BETWEEN SHANNON AND SEMANTIC INFORMATION 195
13.1 INFORMATION COMPRESSION: A GENERAL FORMULA 195 13.2 PATTERN
RECOGNITION AS INFORMATION COMPRESSION: USE OF SYMMETRIES 197 13.3
DEFORMATIONS 199 13.4 REINTERPRETATION OF THE RESULTS OF SECTS.
13.1-13.3 201 14. QUANTUM SYSTEMS 203 14.1 WHY QUANTUM THEORY OF
INFORMATION? 203 14.2 THE MAXIMUM INFORMATION PRINCIPLE 205 14.3 ORDER
PARAMETERS, ENSLAVED MODES AND PATTERNS 211 14.4 INFORMATION OF ORDER
PARAMETERS AND ENSLAVED MODES 214 15. QUANTUM INFORMATION 216 15.1 BASIC
CONCEPTS OF QUANTUM INFORMATION. Q-BITS 216 15.2 PHASE AND DECOHERENCE
218 15.3 REPRESENTATION OF NUMBERS 219 15.4 REGISTER 220 15.5
ENTANGLEMENT 221 16. QUANTUM COMPUTATION 222 16.1 CLASSICAL GATES 222
16.2 QUANTUM GATES 223 16.3 CALCULATION OF THE PERIOD OF A SEQUENCE BY A
QUANTUM COMPUTER . 227 16.4 CODING, DECODING AND BREAKING CODES 229
16.4.1 A LITTLE MATHEMATICS 230 16.4.2 RSA CODING AND DECODING 230
16.4.3 SHOR'S APPROACH, CONTINUED 231 16.5 THE PHYSICS OF SPIN 1/2 233
16.6 QUANTUM THEORY OF A SPIN IN MUTUALLY PERPENDICULAR MAGNETIC FIELDS,
ONE CONSTANT AND ONE TIME DEPENDENT 235 16.7 QUANTUM COMPUTATION AND
SELF-ORGANIZATION 241 17. CONCLUDING REMARKS AND OUTLOOK 242 REFERENCES
244 SUBJECT INDEX 25 1 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Haken, Hermann 1927-2024 |
| author_GND | (DE-588)118824414 |
| author_facet | Haken, Hermann 1927-2024 |
| author_role | aut |
| author_sort | Haken, Hermann 1927-2024 |
| author_variant | h h hh |
| building | Verbundindex |
| bvnumber | BV021649878 |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325 |
| callnumber-search | Q325 |
| callnumber-sort | Q 3325 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 130 UG 3000 UG 3900 |
| ctrlnum | (OCoLC)76481613 (DE-599)BVBBV021649878 |
| dewey-full | 003/.7 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003/.7 |
| dewey-search | 003/.7 |
| dewey-sort | 13 17 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Physik Informatik Psychologie |
| discipline_str_mv | Physik Informatik Psychologie |
| edition | 3., enl. ed. |
| format | Book |
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| id | DE-604.BV021649878 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:02:42Z |
| indexdate | 2024-08-21T00:08:13Z |
| institution | BVB |
| isbn | 3540330216 9783540330219 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014864544 |
| oclc_num | 76481613 |
| open_access_boolean | |
| owner | DE-384 DE-19 DE-BY-UBM DE-739 DE-29T DE-703 DE-11 DE-355 DE-BY-UBR DE-210 |
| owner_facet | DE-384 DE-19 DE-BY-UBM DE-739 DE-29T DE-703 DE-11 DE-355 DE-BY-UBR DE-210 |
| physical | XIV, 257 S. Ill., graph. Darst. 24 cm |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer series in synergetics |
| spelling | Haken, Hermann 1927-2024 Verfasser (DE-588)118824414 aut Information and self-organization a macroscopic approach to complex systems Hermann Haken 3., enl. ed. Berlin [u.a.] Springer 2006 XIV, 257 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Springer series in synergetics 1. ed. appeared as vol. 40 Entropy (Information theory) Self-organizing systems Entropie (DE-588)4014894-4 gnd rswk-swf Komplexes System (DE-588)4114261-5 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Selbst organisierendes System (DE-588)4054424-2 gnd rswk-swf Selbstorganisation (DE-588)4126830-1 gnd rswk-swf Komplexes System (DE-588)4114261-5 s Selbstorganisation (DE-588)4126830-1 s DE-604 Selbst organisierendes System (DE-588)4054424-2 s Entropie (DE-588)4014894-4 s Informationstheorie (DE-588)4026927-9 s 1\p DE-604 2\p DE-604 3\p DE-604 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014864544&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Haken, Hermann 1927-2024 Information and self-organization a macroscopic approach to complex systems Entropy (Information theory) Self-organizing systems Entropie (DE-588)4014894-4 gnd Komplexes System (DE-588)4114261-5 gnd Informationstheorie (DE-588)4026927-9 gnd Selbst organisierendes System (DE-588)4054424-2 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| subject_GND | (DE-588)4014894-4 (DE-588)4114261-5 (DE-588)4026927-9 (DE-588)4054424-2 (DE-588)4126830-1 |
| title | Information and self-organization a macroscopic approach to complex systems |
| title_auth | Information and self-organization a macroscopic approach to complex systems |
| title_exact_search | Information and self-organization a macroscopic approach to complex systems |
| title_exact_search_txtP | Information and self-organization a macroscopic approach to complex systems |
| title_full | Information and self-organization a macroscopic approach to complex systems Hermann Haken |
| title_fullStr | Information and self-organization a macroscopic approach to complex systems Hermann Haken |
| title_full_unstemmed | Information and self-organization a macroscopic approach to complex systems Hermann Haken |
| title_short | Information and self-organization |
| title_sort | information and self organization a macroscopic approach to complex systems |
| title_sub | a macroscopic approach to complex systems |
| topic | Entropy (Information theory) Self-organizing systems Entropie (DE-588)4014894-4 gnd Komplexes System (DE-588)4114261-5 gnd Informationstheorie (DE-588)4026927-9 gnd Selbst organisierendes System (DE-588)4054424-2 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| topic_facet | Entropy (Information theory) Self-organizing systems Entropie Komplexes System Informationstheorie Selbst organisierendes System Selbstorganisation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014864544&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hakenhermann informationandselforganizationamacroscopicapproachtocomplexsystems |