Additional Information
Book Details
Abstract
This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work.
An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems.
- Expert systems techniques and applications are presented for a diverse array of topics including
- Experimental design and decision support
- The integration of machine learning with knowledge acquisition for the design of expert systems
- Process planning in design and manufacturing systems and process control applications
- Knowledge discovery in large-scale knowledge bases
- Robotic systems
- Geograhphic information systems
- Image analysis, recognition and interpretation
- Cellular automata methods for pattern recognition
- Real-time fault tolerant control systems
- CAD-based vision systems in pattern matching processes
- Financial systems
- Agricultural applications
- Medical diagnosis
From the Preface
"This set consists of six, well-integrated volumes on the broad subject of expert systems techniques and applications....All of the contributors to this work are to be highly commended for their splendid contributions that will provide a significant and unique reference for students, research workers, practitioners, computer scientists, and others on the international scene for years to come." --Cornelius T. Leondes
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| 9780080531458_001_WEB | 1 | ||
| Cover | 1 | ||
| Copyright | 5 | ||
| Contents | 6 | ||
| Preface | 24 | ||
| Contributors | 26 | ||
| 01 | 32 | ||
| 02 | 54 | ||
| 03 | 84 | ||
| 04 | 110 | ||
| 05 | 150 | ||
| 06 | 202 | ||
| 07 | 228 | ||
| 08 | 298 | ||
| 9780080531458_002_WEB | 336 | ||
| Cover | 336 | ||
| Copyright | 340 | ||
| Contents | 341 | ||
| Contributors | 359 | ||
| 02aexp-fmv2.pdf | 337 | ||
| 09 | 365 | ||
| 10 | 387 | ||
| 11 | 441 | ||
| 12 | 471 | ||
| 13 | 503 | ||
| 14 | 549 | ||
| 15 | 613 | ||
| 16 | 677 | ||
| 9780080531458_003_WEB | 699 | ||
| Front Cover | 699 | ||
| Expert Systems | 702 | ||
| Copyright Page | 703 | ||
| Contents | 704 | ||
| Contributors | 722 | ||
| Chapter 17. Genetic Image Interpretation | 728 | ||
| I. Introduction | 728 | ||
| II. Preliminaries | 730 | ||
| III. Genetic Algorithms in Computer Vision | 733 | ||
| IV. Genetic Algorithm-Based Image Interpretation Method | 734 | ||
| V. Image Interpretation of Artificially Generated Test Examples | 738 | ||
| VI. Genetic Interpretation of Magnetic Resonance Brain Images | 740 | ||
| VII. Advantages of Genetic Algorithm-Based Image Interpretation | 744 | ||
| References | 746 | ||
| Chapter 18. Automated Visual Assembly Inspection | 750 | ||
| I. Introduction | 750 | ||
| II. The Inspection Algorithm | 754 | ||
| III. Automated Camera and Light Placement | 770 | ||
| IV. Results | 784 | ||
| V. Conclusions | 786 | ||
| References | 788 | ||
| Chapter 19. Multiresolution Invariant Image Recognition | 790 | ||
| I. Image Analysis and New Developments in Multimedia Systems | 791 | ||
| II. Theoretical Aspects of Multiresolution and Cumulant Analysis | 797 | ||
| III. Proposed Invariant Image Representations | 803 | ||
| IV. Multiresolution Neural Network Classifiers of Invariant Representations | 810 | ||
| V. Efficient Multiresolution Texture Classification Scheme | 820 | ||
| VI. Conclusions | 826 | ||
| References | 827 | ||
| Chapter 20. Image Processing for Automatic Roads Determination | 830 | ||
| I. Introduction | 830 | ||
| II. Road Generation | 831 | ||
| III. Road Finding as a Map Estimation Problem | 835 | ||
| IV. High-Level Processing Combining Road Candidates | 845 | ||
| V. Experimental Road Results | 846 | ||
| VI. Conclusions | 856 | ||
| References | 858 | ||
| Chapter 21. Automated Visual Inspection Systems | 860 | ||
| I. Introduction | 860 | ||
| II. Components of an Automated Visual Inspection System | 861 | ||
| III. Image Segmentation | 866 | ||
| IV. Measurements | 870 | ||
| V. Image Transformations | 871 | ||
| VI. Pattern Recognition | 873 | ||
| VII. Three-Dimensional Images | 874 | ||
| VIII. Applications | 875 | ||
| IX. Examples of Automated Visual Inspection Systems | 875 | ||
| X. Conclusions | 888 | ||
| References | 888 | ||
| Chapter 22. Visual Programming Technology in Expert Systems Development | 890 | ||
| I. Introduction | 891 | ||
| II. Visual Knowledge Representation | 892 | ||
| III. Task-Specific Visual Representation | 894 | ||
| IV. Generic Iconic Visual Programming | 907 | ||
| V. Conclusion | 919 | ||
| References | 920 | ||
| Chapter 23. CAD-Based Vision Systems in Pattern Matching Process | 922 | ||
| I. Introduction | 923 | ||
| II. Integrated Vision Systems in Manufacturing Processes | 924 | ||
| III. Computer Models | 930 | ||
| IV. CAD-Based Vision System Design | 939 | ||
| V. Intelligent Techniques for CAD-Based Vision Systems | 944 | ||
| VI. Applications | 949 | ||
| VII. Conclusion | 960 | ||
| References | 960 | ||
| Chapter 24. Cellular Automata Architectures for Pattern Recognition | 964 | ||
| I. Introduction | 965 | ||
| II. Cellular Automata and Pattern Classification | 965 | ||
| III. Hybrid Cellular Automaton–Neural Network Classifier | 967 | ||
| IV. Cellular Automaton-Based, Nearest Neighbor Pattern Classifier | 978 | ||
| V. Very Large Scale Integration Implementation of Cellular Automata Architectures | 993 | ||
| VI. Conclusions | 995 | ||
| References | 995 | ||
| Chapter 25. Machine Intelligent System Techniques for Automatic Harvest Systems | 998 | ||
| I. Introduction | 999 | ||
| II. Automatic Harvest Systems | 1000 | ||
| III. Method of 3D Measuring | 1006 | ||
| IV. Visual Device | 1011 | ||
| V. Development of the Soft Hand | 1014 | ||
| VI. Collision Avoidance Using the Virtual Hand Robot | 1018 | ||
| VII. Conclusion | 1023 | ||
| References | 1023 | ||
| Chapter 26. Integrating Machine Learning with Knowledge Acquisition | 1026 | ||
| I. Introduction | 1026 | ||
| II. The Knowledge Representation Scheme | 1028 | ||
| III. Machine Learning Techniques | 1030 | ||
| IV. Techniques | 1031 | ||
| V. Experimental Evaluation | 1042 | ||
| VI. Conclusions | 1045 | ||
| Appendix | 1045 | ||
| References | 1047 | ||
| Chapter 27. Modeling Human Reasoning Processes under Uncertain Conditions | 1050 | ||
| I. Introduction | 1050 | ||
| II. Probabilistic Models | 1052 | ||
| III. Probabilistic Models for Prediction Problems | 1055 | ||
| IV. Performing What-If Analysis Using Probability Models | 1058 | ||
| V. Strategies for Information Acquisition | 1060 | ||
| VI. Obtaining Probability Models with Composite Attributes | 1063 | ||
| VII. Ongoing and Future Research Issues | 1065 | ||
| References | 1065 | ||
| 9780080531458_004_WEB | 1068 | ||
| Front Cover | 1068 | ||
| Expert Systems: The Technology of Knowledge Management and Decision Making for the 21st Century | 1071 | ||
| Copyright Page | 1072 | ||
| Contents | 1073 | ||
| Contributors | 1091 | ||
| Chapter 28. Devising an Expert System for Pediatric Syndrome Diagnosis | 1097 | ||
| I. Introduction | 1098 | ||
| II. What is a Syndrome? | 1100 | ||
| III. A Good Clinical Sign | 1105 | ||
| IV. Using a Diagnostic Expert System in a New Setting | 1114 | ||
| V. The Problem at the Tertiary Care Center. Moving the Probability | 1114 | ||
| VI. Problems with Using a Clean Bayes’ Approach | 1115 | ||
| VII. Quality of Data | 1117 | ||
| VIII. Subordinate Expert Systems | 1121 | ||
| IX. An Aside: A Different “Expert System” | 1122 | ||
| X. A Syndrome as a “Message,” in Information Theory Terms | 1123 | ||
| XI. Syndromology Expert Systems | 1123 | ||
| XII. Some Philosophical Issues | 1125 | ||
| XIII. Summary | 1128 | ||
| XIV. Conclusion | 1128 | ||
| References | 1129 | ||
| Chapter 29. Automatic Knowledge Discovery in Larger Scale Knowledge–Data Bases | 1133 | ||
| I. Introduction | 1133 | ||
| II. Background and Goal | 1135 | ||
| III. KOSI | 1140 | ||
| IV. IIBR | 1156 | ||
| V. KDD Process and KDD Agents | 1171 | ||
| VI. Concluding Remarks | 1185 | ||
| References | 1186 | ||
| Chapter 30. Efficient Legacy Data Utilization | 1189 | ||
| I. Introduction | 1189 | ||
| II. The Data Migration Problem | 1193 | ||
| III. AM/FM Features | 1194 | ||
| IV. The Object-Inferencing Framework | 1197 | ||
| V. Target Model Data Engineering | 1205 | ||
| VI. Make Feature Process | 1216 | ||
| VII. Testing and Evaluation of the Approach | 1220 | ||
| VIII. Conclusions | 1222 | ||
| References | 1223 | ||
| Chapter 31. Investment Decision Making | 1225 | ||
| I. Introduction | 1225 | ||
| II. Customer Profile and Project Evaluation | 1227 | ||
| III. Unido Methodology | 1232 | ||
| IV. Heuristic Decision Strategy | 1233 | ||
| V. Risk-Bearing Attitude | 1240 | ||
| VI. Multicriteria Analysis | 1243 | ||
| VII. Sensitivity Analysis | 1249 | ||
| VIII. Conclusion | 1250 | ||
| References | 1250 | ||
| Chapter 32. Intelligent Systems Control in Manufacturing Cells | 1253 | ||
| I. Introduction | 1253 | ||
| II. Literature Review | 1254 | ||
| III. Architecture of Controller | 1257 | ||
| IV. System Description and Simulation Model | 1259 | ||
| V. Development of Controller | 1262 | ||
| VI. Experiments and Results | 1266 | ||
| VII. Concluding Remarks | 1270 | ||
| References | 1271 | ||
| Chapter 33. Knowledge-Based Approach for Automating Web Publishing from Databases | 1273 | ||
| I. Introduction | 1273 | ||
| II. Automating HTML Page Generation | 1275 | ||
| III. Knowledge Representation Scheme for KHDG | 1278 | ||
| IV. Implementation of KHDG | 1283 | ||
| V. A Prototype. Smart Stock Information Agent | 1287 | ||
| VI. Summary | 1290 | ||
| References | 1290 | ||
| Chapter 34. Neural Networks for Economic Forecasting Problems | 1293 | ||
| I. Introduction | 1293 | ||
| II. Univariate Time-Series Forecasting | 1293 | ||
| III. Multivariate Prediction | 1295 | ||
| IV. Hybrid Systems | 1305 | ||
| V. Recurrent Neural Networks | 1312 | ||
| VI. Summary | 1313 | ||
| References | 1313 | ||
| Chapter 35. Determination of Principal Components in Data | 1317 | ||
| I. What is Principal Component Analysis? | 1318 | ||
| II. Principal Component Analysis Neural Networks | 1328 | ||
| III. Biological Background of Principal Component Analysis Neural Networks | 1348 | ||
| IV. Techniques | 1350 | ||
| V. Speeding up Learning of Principal Component Analysis Neural Networks | 1354 | ||
| VI. Minor Component Analysis Neural Networks | 1361 | ||
| VII. Nonlinear Principal Component Analysis Neural Networks | 1364 | ||
| References | 1374 | ||
| Chapter 36. Time-Series Prediction | 1378 | ||
| I. Introduction | 1379 | ||
| II. Time-Series Prediction Using Multilayer Perceptrons | 1381 | ||
| III. Time-Series Prediction Using Finite Impulse Response Multilayer Perceptrons | 1403 | ||
| IV. Time-Series Prediction Using Recurrent Neural Networks | 1414 | ||
| V. Discussions | 1430 | ||
| References | 1431 | ||
| 9780080531458_005_WEB | 1434 | ||
| Front Cover | 1434 | ||
| EXPERT SYSTEMS: The Technology of Knowledge Management for the 21st Century | 1437 | ||
| Copyright Page | 1438 | ||
| CONTENTS | 1439 | ||
| CONTRIBUTORS | 1457 | ||
| Chapter 37. Hybrid Expert Systems: AnApproach to Combining Neural Computation and Rule-Based Reasoning | 1463 | ||
| I. Introduction | 1464 | ||
| II. Hybrid Visual Data Acquisition System | 1465 | ||
| III. Pictorial Form of Explanation | 1476 | ||
| IV. Neural Forward Chaining | 1482 | ||
| V. Neural Forward Chaining and FPGAs | 1491 | ||
| VI. Discussion | 1496 | ||
| References | 1500 | ||
| Chapter 38. POPFNNS: Fuzzy Neural Techniques for Rule-Based Identification in Expert Systems | 1503 | ||
| I. Literature Survey | 1504 | ||
| II. POPFNN Models | 1517 | ||
| III. Learning Algorithms for the Introduced Fuzzy Neural Networks | 1531 | ||
| IV. Applications of Fuzzy Neural Networks | 1541 | ||
| V. Conclusions | 1554 | ||
| References | 1554 | ||
| Chapter 39. Preventive Quality Management | 1561 | ||
| I. Introduction | 1562 | ||
| II. IPQM | 1566 | ||
| III. Method | 1568 | ||
| IV. Realization | 1586 | ||
| V. Related Work | 1595 | ||
| VI. Discussion | 1598 | ||
| References | 1601 | ||
| Chapter 40. Distributed Logic Processors in Process Identification | 1605 | ||
| I. Introduction | 1605 | ||
| II. Distributed Logic Processors | 1607 | ||
| III. Gradient-Based Learning | 1615 | ||
| IV. Learning Automata-Based Learning | 1620 | ||
| V. Modeling of Flue Gas Emissions | 1629 | ||
| VI. Conclusions and Discussion | 1641 | ||
| References | 1642 | ||
| Chapter 41. Knowledge Representation By Means of Multilayer Perceptrons | 1645 | ||
| I. Introduction | 1645 | ||
| II. KRFs Considered | 1647 | ||
| III. Issues in Combining SP and NNs | 1651 | ||
| IV. Applications | 1665 | ||
| V. Conclusions | 1670 | ||
| References | 1671 | ||
| Chapter 42. A Guide to Research in Assumption-Based Truth Maintenance System Constraint Satisfaction | 1673 | ||
| I. Introduction | 1673 | ||
| II. Background to Reason Maintenance | 1681 | ||
| III. Improving the Performance of Assumption-Based Truth Maintenance System Problem Solvers | 1688 | ||
| IV. Global Perspective | 1702 | ||
| V. Conclusions | 1703 | ||
| References | 1704 | ||
| Chapter 43. Method for Utilization of Previous Experience in Design Expert Systems | 1707 | ||
| I. Introduction | 1707 | ||
| II. Framework of Inductive Prediction by Analogy | 1708 | ||
| III. Analogy Using Taxonomic Information | 1709 | ||
| IV. Algorithm of Inductive Prediction by Analogy | 1711 | ||
| V. Applications in Logic Programming | 1712 | ||
| VI. Classification Problem in Molecular Biology | 1716 | ||
| VII. Discussion and Related Work | 1721 | ||
| VIII. Conclusion | 1722 | ||
| References | 1722 | ||
| Chapter 44. Model-Based Process Fault Diagnosis | 1725 | ||
| I. Introduction | 1725 | ||
| II. Process Fault Diagnosis Techniques Based on Qualitative Models | 1729 | ||
| III. Process Fault Diagnosis Techniques Based on Fuzzy Models | 1753 | ||
| IV. Process Fault Diagnosis Techniques Based on Approximate Quantitative Models | 1774 | ||
| V. Discussions | 1785 | ||
| References | 1786 | ||
| 9780080531458_006_WEB | 1789 | ||
| Front Cover | 1789 | ||
| EXPERT SYSTEMS: The Technology of Knowledge Management and Decision Making for the 21st Century | 1792 | ||
| Copyright Page | 1793 | ||
| CONTENTS | 1794 | ||
| CONTRIBUTORS | 1812 | ||
| Chapter 45. Automation of Concept Development | 1818 | ||
| I. Introduction | 1819 | ||
| II. Motivation | 1820 | ||
| III. Related Work and Problems for Concept Development | 1821 | ||
| IV. Knowledge Representation | 1822 | ||
| V. Concept Development Mechanism | 1824 | ||
| VI. Discussion of the Classification of Decision Support Systems | 1833 | ||
| VII. Conclusion | 1834 | ||
| Appendix | 1837 | ||
| References | 1841 | ||
| Chapter 46. Methodology for Building Case-Based Reasoning Systems in Ill-Structured Optimization Domains | 1844 | ||
| I. Introduction | 1844 | ||
| II. Scheduling Problem | 1847 | ||
| III. Modeling the Optimization Task | 1849 | ||
| IV. Cabins: Case-Based Optimization Approach | 1852 | ||
| V. Experiments | 1863 | ||
| VI. Conclusions | 1872 | ||
| References | 1872 | ||
| Chapter 47. The Trainer System: Applying QR Techniques to Intelligent Tutoring Systems | 1876 | ||
| I. Introduction | 1878 | ||
| II. System Categorizations Framework | 1882 | ||
| III. Instructional Systems Based on Qualitative Analysis | 1889 | ||
| IV. Diagnostic Systems Based on Qualitative Analysis | 1897 | ||
| V. Observations and Discussions | 1901 | ||
| VI. Design of the Trainer System | 1907 | ||
| VII. Formative Evaluation | 1928 | ||
| VIII. Conclusion | 1944 | ||
| References | 1945 | ||
| Chapter 48. Structuring Expert Control Using the Integrated Process Supervision Architecture | 1950 | ||
| Introduction | 1951 | ||
| I. Intelligent Control and Supervision | 1951 | ||
| II. Integrated Process Supervision | 1956 | ||
| III. Realization of the IPS | 1962 | ||
| IV. Rule-Based Process Supervision | 1974 | ||
| V. Real-Time Integrated Process Supervision | 1984 | ||
| VI. Present and Future Developments | 1998 | ||
| Conclusion | 2002 | ||
| References | 2004 | ||
| Chapter 49. Tap: An Inquiry Teaching Shell Using Both Rule-Based and State-Space Approaches | 2008 | ||
| I. Introduction | 2009 | ||
| II. Instructional Planning and Inquiry Teaching | 2013 | ||
| III. TAP: An ITS Architecture to Plan Inquiry Dialogue | 2022 | ||
| IV. Planning in TAP-2 | 2027 | ||
| V. Domain Case Study I: PADI-2 | 2038 | ||
| VI. Domain Case Study II: FT-TAP | 2059 | ||
| VII. Conclusion and Future Directions | 2067 | ||
| References | 2070 | ||
| Chapter 50. Self Teaching and Exploratory Task-Learning Methods in Unknown Environments and Applications in Robotic Skills | 2074 | ||
| I. Introduction | 2075 | ||
| II. Neural Network-Based Learning Architecture | 2077 | ||
| III. Force Control Skill | 2086 | ||
| IV. Learning to Navigate a Mobile Robot | 2092 | ||
| V. Neural Network-Based Local Mapping | 2094 | ||
| VI. Conclusions | 2098 | ||
| References | 2099 | ||
| INDEX | 2102 |