Intelligent Spatial Decision Support Systems - Brossura

Leung, Yee

Brossura

ISBN 10: 3642645216 ISBN 13: 9783642645211

Casa editrice: Springer, 2011

Riassunto

This book lays a foundation on the conceptual, theoretical, system-development, and application perspectives of intelligent spatial decision support systems. Departing from conventional approaches that only concentrate on model-data integration, the author places emphasis on the artificial intelligence approach to spatial decision support.

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Contenuti

1 Introduction.- 1.1 On Complexity of Spatial Decision Making.- 1.2 Basic Notions of Spatial Decision Support Systems.- 1.3 A General Architecture for Intelligent Spatial Decision Support Systems.- 1.4 Purpose and Structure of the Monograph.- 2 Symbolic Approaches to Spatial Knowledge Representation and Inference.- 2.1 A Note on Knowledge Representation and Inference.- 2.2 Propositional and Predicate Logic.- 2.2.1 Propositional Logic.- 2.2.2 Predicate Logic.- 2.3 Production Systems.- 2.3.1 Rules and Decision Trees.- 2.3.2 Inference in Production Systems.- 2.4 Semantic Networks.- 2.4.1 Basic Features of a Semantic Network.- 2.4.2 Semantic-Network Representations of Spatial Relations.- 2.4.3 Multiple-Predicate Representations by Partitioned Semantic Networks.- 2.4.4 Inference in Semantic Networks.- 2.5 Frames.- 2.5.1 Frame-Based Representation of Knowledge.- 2.5.2 Frame-Based Spatial Inference.- 2.6 Object-Oriented Approach.- 2.6.1 Hierarchical Representation of Spatial Knowledge.- 2.6.2 Inheritance in an Object Hierarchy.- 2.7 A Note on Hybrid Representations.- 3 Fuzzy Logic Approaches to Spatial Knowledge Representation and Inference.- 3.1 A Note on Fuzzy Logic.- 3.2 Fuzzy Propositions.- 3.2.1 Fuzzy Propositions and their Translations.- 3.2.2 Composition of Fuzzy Propositions.- 3.3 Fuzzy IF-THEN Propositions and their Translations.- 3.4 Fuzzy Inference.- 3.4.1 On Fuzzy Spatial Inference.- 3.4.2 Rules of Inference Based on the Entailment Principle.- 3.4.3 Rules of Inference Based on the Extension Principle.- 3.4.4 Fuzzy Inference Based on Truth-value.- 3.4.5 Inferences with Quantified Propositions.- 3.5 Linguistic Approximation in Fuzzy Inference.- 3.6 Fuzzy Rule-Based Inference Requiring Precise Output.- 3.6.1 Inference Involving Precise Output and Fuzzy Input.- 3.6.2 Inference Involving Precise Output and Precise Input.- 4 Management of Uncertainty in Knowledge Representation and Inference.- 4.1 On Measures of Confidence.- 4.2 Probabilistic Approaches.- 4.2.1 Measure of Confidence.- 4.2.2 Probabilistic Inference.- 4.2.3 Inference by Subjective Bayesian Methods.- 4.3 Certainty Factor.- 4.3.1 Measure of Confidence.- 4.3.2 Combination of Evidence and Propagation of Uncertainty.- 4.4 Shafer-Dempster’s Belief Functions.- 4.4.1 Measure of Confidence.- 4.4.2 Combination of Evidence.- 4.5 Possibility-Necessity Approach.- 4.5.1 Measure of Confidence.- 4.5.2 Macro-level Fuzzy Inference.- 4.5.3 A Synthesis of Truth Values and the Possibility-Necessity Measure.- 4.6 A Note on the Theory of Inclusion.- 5 Neural Network Approaches to Spatial Knowledge Representation and Inference.- 5.1 A Remark on Symbolic and Neural Network Approaches to Knowledge Representation and Inference.- 5.2 A Brief Review of Neural Networks Research.- 5.3 Spatial Knowledge Representation and Inference by Feedforward Neural Networks.- 5.3.1 logical Processing with Simple Feedforward Neural Networks.- 5.3.2 Fuzzy Logical Processing with Simple Feedforward Neural Networks.- 5.3.3 Knowledge-based Feedforward Neural Networks.- 5.3.4 Applications of Feedforward Neural Networks in Spatial Information Processing.- 5.4 Spatial Knowledge Representation and Inference by Recurrent Neural Networks.- 5.4.1 Autoassociative Memories ― Hopfield Networks as an Example.- 5.4.2 Heteroassociative Memories ― Bidirectional Associative Memories as an Example.- 5.5 A Note on Hybrid Spatial Decision Support Systems.- 6 Knowledge Acquisition for Spatial Inference ― The Case of Genetic Algorithms.- 6.1 The Necessity of Automatic Knowledge Acquisition.- 6.2 A Brief Note on Genetic Algorithms.- 6.3 A Formalism of Canonical Genetic Algorithms.- 6.4 Rule Learning Using Genetic Algorithms.- 6.4.1 Learning Precise Rules in Expert Systems Using Genetic Algorithms.- 6.4.2 Some Empirical Studies.- 6.4.3 Learning Fuzzy Rules in Expert Systems Using Genetic Algorithms.- 6.5 Evolving Neural Networks Using Genetic Algorithms.- 6.5.1 Learning of Connection Weights with Fixed Topology Using Genetic Algorithms.- 6.5.2 Evolving Neural Network Topologies by Genetic Algorithms.- 6.6 A Remark on Genetic Algorithms.- 7 Spatial Data Models and Data Structures.- 7.1 A Note on Data Models and Data Structures.- 7.2 Spatial Data Models and Data Structures within the Layer-Viewed Framework.- 7.2.1 Vector Data Models and Data Structures.- 7.2.2 Raster Data Models and Data Structures.- 7.3 Relational Database for Precise Data.- 7.3.1 Basic Concepts of Relational Model and Relational Database.- 7.3.2 Relational Languages.- 7.3.3 Relation Query Language.- 7.4 Fuzzy Relational Model and Database.- 7.4.1 Fuzzy Relational Database.- 7.4.2 Fuzzy Queries and Fuzzy Relational Algebra.- 7.5 Issues of Spatial Database Accuracy.- 7.5.1 Error Models for Spatial Features.- 7.5.2 Spatial Queries under Certainty and Uncertainty ― Point-in-Polygon Query as an Example.- 7.6 Spatial Data Models and Data Structures within the Object-Viewed Framework.- 8 Management of Models in Spatial Decision Support Systems.- 8.1 The Necessity of a Systematic Management of Models in Spatial Decision Support Systems.- 8.2 Model Classification and Organization.- 8.2.1 Model Classification Based on Decision Problems.- 8.2.2 Model Classification Based on Technical Conditions.- 8.2.3 Formulation of Specific Spatial Optimization Models.- 8.3 Model-Data Linkage ― Spatial Network Analysis as a Case Study.- 8.3.1 On Model-data Linkage.- 8.3.2 Spatial Network Analysis in the Context of Geographical Information Systems.- 9 An Expert System Shell for Building Spatial-Decision- Support-System Development Tool.- 9.1 On the Architecture of a Spatial-Decision-Support-System Development Tool.- 9.2 The Fuzzy-Logic-Based Expert System Shell (FLESS) ― The Nerve Center of the Spatial-Decision-Support-System Development Tool.- 9.2.1 Knowledge Base Development.- 9.2.2 Fuzzy Knowledge Base.- 9.2.3 Tracing.- 9.2.4 Data Exchange.- 9.2.5 Operations.- 9.3 Equipping FLESS with Automatic Knowledge-Acquisition Capabilities.- 9.4 Application of FLESS in Building Expert Systems for Spatial Classification Problems.- 9.4.1 A Note on Spatial Classification.- 9.4.2 An Expert System for Land-type Classification.- 10 A Spatial Decision Support System for Flood Simulation and Damage Assessment Using FLESS.- 10.1 History of Flooding in Sun Hugou Watershed.- 10.2 Overall Design of the Flood Simulation and Damage Assessment Process.- 10.2.1 Database Construction.- 10.2.2 Data Manipulation.- 10.2.3 Preliminary Flood Assessment.- 10.2.4 Flood Assessment Based on Remote Sensing Techniques.- 10.2.5 Flood Simulation and Damage Assessment Based on Process Models.- 10.3 The Flood Simulation and Damage Assessment Decision Support System.- 10.3.1 Rules for Preliminary Analysis and Initial Assessment.- 10.3.2 Interactive Utilization of Rules and Models.- 11 An Object-Oriented Expert System Shell for Building Spatial Decision Support Systems.- 11.1 A Note on Object-Oriented Approach to Integrative Spatial Decision Support Systems.- 11.2 The Architecture of an Object-Oriented Expert System Shell for Spatial Inference.- 11.3 An Expert System for Solving Hierarchical Programming Problems.- 11.3.1 An Hierarchical Programming Problem and its Solution.- 11.3.2 An Object-oriented Expert System Implementation.- 11.4 A Pedagogic Example.- 12 Conclusion.- 12.1 Summary.- 12.2 Outlook and Research Directions.- References.- List of Figures.- List of Tables.

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2011
• ISBN 10: 3642645216
• ISBN 13: 9783642645211
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 492
• Contatto del produttore: non disponibile