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Fuzzy Cognitive Maps : Best Practices and Modern Methods
| Fuzzy Cognitive Maps : Best Practices and Modern Methods |
| Autore | Giabbanelli Philippe J |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (228 pages) |
| Altri autori (Persone) | NápolesGonzalo |
| ISBN | 3-031-48963-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Contents -- Contributors -- Acronyms -- 1 Defining and Using Fuzzy Cognitive Mapping -- 1.1 Introduction -- 1.2 Three Equivalent Definitions -- 1.2.1 FCMs as Mental Models -- 1.2.2 FCMs as Mathematical Objects -- 1.2.3 FCMs as Simulation Tools -- 1.3 A Typology of Uses -- 1.3.1 FCMs as Expert Systems -- 1.3.2 FCMs in Collective Intelligence -- 1.3.3 FCMs as Boundary Objects to Support Learning -- 1.3.4 FCMs as Prediction Models -- References -- 2 Creating an FCM with Participants in an Interview or Workshop Setting -- 2.1 Decision Factors -- 2.1.1 Individual Versus Group Modeling -- 2.1.2 Facilitator Versus Participant Mapping -- 2.1.3 Hand-Drawn Models Versus Modeling Software -- 2.1.4 Pre-defined, Open-Ended Concepts or Hybrid Approach -- 2.2 Data Collection -- 2.2.1 Creating a Parsimonious Model and Weighting Connections -- 2.2.2 Participant Recruitment -- 2.2.3 Facilitation Considerations -- 2.3 Conclusions -- References -- 3 Principles of Simulations with FCMs -- 3.1 Introduction: Revisiting the Reasoning Mechanism -- 3.2 Activation Functions -- 3.3 Convergence: A Mathematical Perspective -- 3.4 Convergence: A Simulation Approach -- 3.5 A Detailed Example in Python -- 3.6 Exercises -- References -- 4 Hybrid Simulations -- 4.1 Introduction -- 4.2 Rationale for a Hybrid ABM/FCM Simulation -- 4.3 Main Steps to Design a Hybrid ABM/FCM Simulation -- 4.4 Example of Study Design and Python Implementation -- 4.5 Scaling-Up Simulations Using Parallelism -- 4.6 Exercises -- References -- 5 Analysis of Fuzzy Cognitive Maps -- 5.1 Why Analyze Fuzzy Cognitive Maps? -- 5.2 What Are the Important Concepts? -- 5.2.1 Transmitter, Receiver, and Ordinary Concepts -- 5.2.2 Centrality Measures -- 5.3 Validating the Facilitation Process -- 5.3.1 Number of Concepts and Relationships -- 5.3.2 Receiver-Transmitter Ratio.
5.3.3 Metrics Based on Shortest Paths -- 5.3.4 Clustering Coefficient -- 5.3.5 Density -- 5.3.6 Feedback Loops -- 5.4 Conclusion -- 5.5 Exercises -- References -- 6 Extensions of Fuzzy Cognitive Maps -- 6.1 Why Do We Extend Fuzzy Cognitive Maps? -- 6.2 Interval-Valued Fuzzy Cognitive Maps -- 6.2.1 Example Interval-Valued Fuzzy Cognitive Map Inference -- 6.3 Time-Interval Fuzzy Cognitive Maps -- 6.3.1 Example Time-Interval Fuzzy Cognitive Map Inference -- 6.4 Extended-Fuzzy Cognitive Maps -- 6.4.1 Example Extended-Fuzzy Cognitive Map Inference -- 6.5 Trends and Future of Extensions of Fuzzy Cognitive Maps -- 6.6 Exercises -- References -- 7 Creating FCM Models from Quantitative Data with Evolutionary Algorithms -- 7.1 Introduction -- 7.2 Representing the Genome -- 7.2.1 Transformations Between Vector and Matrix -- 7.2.2 Constraints -- 7.3 Evaluation -- 7.4 Genetic Algorithms -- 7.5 Analysis -- 7.6 CMA-ES -- References -- 8 Advanced Learning Algorithm to Create FCM Models From Quantitative Data -- 8.1 Introduction -- 8.2 Hybrid Fuzzy Cognitive Map Model -- 8.3 Training the Hybrid FCM Model -- 8.4 Optimizing the Hybrid FCM Model -- 8.4.1 Detecting Superfluous Relationships -- 8.4.2 Calibrating the Sigmoid Offset -- 8.4.3 Calibrating the Weights -- 8.5 How to Use These Algorithms in Practice? -- 8.6 Applying the FCM Model to Real-World Data -- 8.6.1 Sensitivity to the Sigmoid Function Parameters -- 8.6.2 Comparison with Other Learning Approaches -- 8.7 Further Readings -- 8.8 Exercises -- References -- 9 Introduction to Fuzzy Cognitive Map-Based Classification -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Notions of Classification and Features -- 9.2.2 Preliminary Processing -- 9.2.3 Performance Metrics -- 9.3 The FCM-Based Classification Model -- 9.3.1 Basic FCM Architecture for Data Classification -- 9.3.2 Genetic Algorithm-Based Optimization. 9.3.3 How Does the Model Classify New Instances? -- 9.4 Classification Toy Case Study -- 9.4.1 Data Description -- 9.4.2 Classifier Implementation -- 9.4.3 Classification-Overall Quality -- 9.5 Further Readings -- 9.6 Exercises -- References -- 10 Addressing Accuracy Issues of Fuzzy Cognitive Map-Based Classifiers -- 10.1 Introduction -- 10.2 Long-Term Cognitive Network-Based Classifier -- 10.2.1 Generalizing the Traditional FCM Formalism -- 10.2.2 Recurrence-Aware Decision Model -- 10.2.3 Learning Algorithm for LTCN-Based Classifiers -- 10.3 Model-Dependent Feature Importance Measure -- 10.4 How to Use the LTCN-Based Classifier in Practice? -- 10.5 Empirical Evaluation of the LTCN Classifier -- 10.5.1 Pattern Classification Datasets -- 10.5.2 Does the LTCN Classifier Outperform the FCM Classifier? -- 10.5.3 Hyperparameter Sensitivity Analysis -- 10.5.4 Comparison of LTCN with State-of-the-Art Classifiers -- 10.6 Illustrative Case Study: Phishing Dataset -- 10.7 Further Readings -- 10.8 Exercises -- References -- Index. |
| Record Nr. | UNINA-9910806193303321 |
Giabbanelli Philippe J
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| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modelling and Simulation of Human-Environment Interactions
| Modelling and Simulation of Human-Environment Interactions |
| Autore | Giabbanelli Philippe J |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (164 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
goal frames
restoration decision-making rules restoration decision-making processes mixed qualitative and quantitative data collection and analysis farmer stakeholders Central Malawi computer modeling human simulation social simulation sustainability development studies assemblage theory ontology epistemology ethics agent-based modeling housing markets Urban Shrinkage cities Detroit GIS agent-based model model development IoT sensors smart cities real-time data MARS simulation correction decision support systems urban planning multimodal travel human responses quantitative modeling water resources planning water availability water shortage drought Congo Basin Lake Chad climate change water migrations conflicts gender resilient development modeling hybrid modeling hybrid simulation usability high school education physics education user experience |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557204003321 |
|
Giabbanelli Philippe J
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||