Neural network learning and expert systems /:
"Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks a...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©1993.
©1993 |
| Schriftenreihe: | Bradford Book Ser.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more. In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs." |
| Beschreibung: | "Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks." "A Bradford Book." |
| Beschreibung: | 1 online resource (xvi, 365 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 349-359) and index. |
| ISBN: | 0585040281 9780585040288 0262071452 9780262071451 9780262273404 0262273403 9780262527897 0262527898 |
Internformat
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| 100 | 1 | |a Gallant, Stephen I. |0 http://id.loc.gov/authorities/names/n92058126 | |
| 245 | 1 | 0 | |a Neural network learning and expert systems / |c Stephen I. Gallant. |
| 260 | |a Cambridge, Mass. : |b MIT Press, |c ©1993. | ||
| 264 | 4 | |c ©1993 | |
| 300 | |a 1 online resource (xvi, 365 pages) : |b illustrations | ||
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| 490 | 1 | |a A Bradford Book Ser. | |
| 500 | |a "Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks." | ||
| 500 | |a "A Bradford Book." | ||
| 504 | |a Includes bibliographical references (pages 349-359) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | 0 | |g 1. |t Introduction and important definitions -- |g 2. |t Representation issues -- |g 3. |t Perceptron learning and the pocket algorithm -- |g 4. |t Winner-take-all groups or linear machines -- |g 5. |t Autoassociators and one-shot learning -- |g 6. |t Mean squared error (MSE) algorithms -- |g 7. |t Unsupervised learning -- |g 8. |t The distributed method and radial basis functions -- |g 9. |t Computational learning theory and the BRD algorithm -- |g 10. |t Constructive algorithms -- |g 11. |t Backpropagation -- |g 12. |t Backpropagation : variations and applications -- |g 13. |t Simulated annealing and boltzmann machines -- |g 14. |t Expert systems and neural networks -- |g 15. |t Details of the MACIE system -- |g 16. |t Noise, redundancy, fault detection, and bayesian decision theory -- |g 17. |t Extracting rules from networks. |
| 520 | 3 | |a "Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more. In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs." | |
| 546 | |a English. | ||
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Expert systems (Computer science) |0 http://id.loc.gov/authorities/subjects/sh85046450 | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Systèmes experts (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 0 | 7 | |a Expert systems (Computer science) |2 cct |
| 650 | 0 | 7 | |a Inteligencia Artificial. |2 cct |
| 650 | 0 | 7 | |a Neural networks (Computer science) |2 cct |
| 650 | 7 | |a Expert systems (Computer science) |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Inteligencia Artificial. |2 larpcal | |
| 650 | 7 | |a Systèmes experts (informatique) |2 ram | |
| 650 | 7 | |a Réseaux neuronaux (informatique) |2 ram | |
| 650 | 7 | |a Intelligence artificielle. |2 ram | |
| 653 | 0 | |a Expert systems. | |
| 653 | |a COMPUTER SCIENCE/Machine Learning & Neural Networks | ||
| 758 | |i has work: |a Neural network learning and expert systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFwYkK39XYqYJWvjykfj83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Gallant, Stephen I. |t Neural network learning and expert systems. |d Cambridge, Mass. : MIT Press, ©1993 |z 0262071452 |w (DLC) 92020864 |w (OCoLC)26094686 |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBU-ocm43474728 |
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| _version_ | 1816796894037278720 |
| adam_text | |
| any_adam_object | |
| author | Gallant, Stephen I. |
| author_GND | http://id.loc.gov/authorities/names/n92058126 |
| author_facet | Gallant, Stephen I. |
| author_role | |
| author_sort | Gallant, Stephen I. |
| author_variant | s i g si sig |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.87 .G35 1993eb |
| callnumber-search | QA76.87 .G35 1993eb |
| callnumber-sort | QA 276.87 G35 41993EB |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-4-EBU |
| contents | Introduction and important definitions -- Representation issues -- Perceptron learning and the pocket algorithm -- Winner-take-all groups or linear machines -- Autoassociators and one-shot learning -- Mean squared error (MSE) algorithms -- Unsupervised learning -- The distributed method and radial basis functions -- Computational learning theory and the BRD algorithm -- Constructive algorithms -- Backpropagation -- Backpropagation : variations and applications -- Simulated annealing and boltzmann machines -- Expert systems and neural networks -- Details of the MACIE system -- Noise, redundancy, fault detection, and bayesian decision theory -- Extracting rules from networks. |
| ctrlnum | (OCoLC)43474728 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. 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| id | ZDB-4-EBU-ocm43474728 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:55Z |
| institution | BVB |
| isbn | 0585040281 9780585040288 0262071452 9780262071451 9780262273404 0262273403 9780262527897 0262527898 |
| language | English |
| lccn | 92020864 |
| oclc_num | 43474728 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 365 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 1993 |
| publishDateSearch | 1993 |
| publishDateSort | 1993 |
| publisher | MIT Press, |
| record_format | marc |
| series | Bradford Book Ser. |
| series2 | A Bradford Book Ser. |
| spelling | Gallant, Stephen I. http://id.loc.gov/authorities/names/n92058126 Neural network learning and expert systems / Stephen I. Gallant. Cambridge, Mass. : MIT Press, ©1993. ©1993 1 online resource (xvi, 365 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier A Bradford Book Ser. "Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks." "A Bradford Book." Includes bibliographical references (pages 349-359) and index. Print version record. 1. Introduction and important definitions -- 2. Representation issues -- 3. Perceptron learning and the pocket algorithm -- 4. Winner-take-all groups or linear machines -- 5. Autoassociators and one-shot learning -- 6. Mean squared error (MSE) algorithms -- 7. Unsupervised learning -- 8. The distributed method and radial basis functions -- 9. Computational learning theory and the BRD algorithm -- 10. Constructive algorithms -- 11. Backpropagation -- 12. Backpropagation : variations and applications -- 13. Simulated annealing and boltzmann machines -- 14. Expert systems and neural networks -- 15. Details of the MACIE system -- 16. Noise, redundancy, fault detection, and bayesian decision theory -- 17. Extracting rules from networks. "Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more. In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs." English. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Réseaux neuronaux (Informatique) Systèmes experts (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Expert systems (Computer science) cct Inteligencia Artificial. cct Neural networks (Computer science) cct Expert systems (Computer science) fast Neural networks (Computer science) fast Inteligencia Artificial. larpcal Systèmes experts (informatique) ram Réseaux neuronaux (informatique) ram Intelligence artificielle. ram Expert systems. COMPUTER SCIENCE/Machine Learning & Neural Networks has work: Neural network learning and expert systems (Text) https://id.oclc.org/worldcat/entity/E39PCFwYkK39XYqYJWvjykfj83 https://id.oclc.org/worldcat/ontology/hasWork Print version: Gallant, Stephen I. Neural network learning and expert systems. Cambridge, Mass. : MIT Press, ©1993 0262071452 (DLC) 92020864 (OCoLC)26094686 Bradford Book Ser. FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3114 Volltext |
| spellingShingle | Gallant, Stephen I. Neural network learning and expert systems / Bradford Book Ser. Introduction and important definitions -- Representation issues -- Perceptron learning and the pocket algorithm -- Winner-take-all groups or linear machines -- Autoassociators and one-shot learning -- Mean squared error (MSE) algorithms -- Unsupervised learning -- The distributed method and radial basis functions -- Computational learning theory and the BRD algorithm -- Constructive algorithms -- Backpropagation -- Backpropagation : variations and applications -- Simulated annealing and boltzmann machines -- Expert systems and neural networks -- Details of the MACIE system -- Noise, redundancy, fault detection, and bayesian decision theory -- Extracting rules from networks. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Réseaux neuronaux (Informatique) Systèmes experts (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Expert systems (Computer science) cct Inteligencia Artificial. cct Neural networks (Computer science) cct Expert systems (Computer science) fast Neural networks (Computer science) fast Inteligencia Artificial. larpcal Systèmes experts (informatique) ram Réseaux neuronaux (informatique) ram Intelligence artificielle. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85046450 |
| title | Neural network learning and expert systems / |
| title_alt | Introduction and important definitions -- Representation issues -- Perceptron learning and the pocket algorithm -- Winner-take-all groups or linear machines -- Autoassociators and one-shot learning -- Mean squared error (MSE) algorithms -- Unsupervised learning -- The distributed method and radial basis functions -- Computational learning theory and the BRD algorithm -- Constructive algorithms -- Backpropagation -- Backpropagation : variations and applications -- Simulated annealing and boltzmann machines -- Expert systems and neural networks -- Details of the MACIE system -- Noise, redundancy, fault detection, and bayesian decision theory -- Extracting rules from networks. |
| title_auth | Neural network learning and expert systems / |
| title_exact_search | Neural network learning and expert systems / |
| title_full | Neural network learning and expert systems / Stephen I. Gallant. |
| title_fullStr | Neural network learning and expert systems / Stephen I. Gallant. |
| title_full_unstemmed | Neural network learning and expert systems / Stephen I. Gallant. |
| title_short | Neural network learning and expert systems / |
| title_sort | neural network learning and expert systems |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Expert systems (Computer science) http://id.loc.gov/authorities/subjects/sh85046450 Réseaux neuronaux (Informatique) Systèmes experts (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Expert systems (Computer science) cct Inteligencia Artificial. cct Neural networks (Computer science) cct Expert systems (Computer science) fast Neural networks (Computer science) fast Inteligencia Artificial. larpcal Systèmes experts (informatique) ram Réseaux neuronaux (informatique) ram Intelligence artificielle. ram |
| topic_facet | Neural networks (Computer science) Expert systems (Computer science) Réseaux neuronaux (Informatique) Systèmes experts (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Inteligencia Artificial. Systèmes experts (informatique) Réseaux neuronaux (informatique) Intelligence artificielle. |
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| work_keys_str_mv | AT gallantstepheni neuralnetworklearningandexpertsystems |