1.
図書
Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain (eds.)
2.
図書
Vasile Palade, Robert J. Howlett, Lakhmi Jain (eds.)
3.
ＥＢ
Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
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4.
ＥＢ
Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade
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Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
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5.
ＥＢ
Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, J?rg Siekmann
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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6.
ＥＢ
Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, J?rg Siekmann
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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7.
ＥＢ
Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
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8.
ＥＢ
Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, J?rg Siekmann
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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9.
ＥＢ
Lakhmi C. Jain, L. C. Jain, Nikhil R. Pal, Xindong Wu, Nikhil Pal
出版情報:
Springer eBooks Computer Science , Springer London, 2005
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Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
Clustering and Visualization of Retail Market Baskets / Chapter 3：
Segmentation of Continuous Data Streams Based on a Change Detection Methodology / Chapter 4：
Instance Selection Using Evolutionary Algorithms: An Experimental Study / Chapter 5：
Using Cooperative Coevolution for Data Mining of Bayesian Networks / Chapter 6：
Knowledge Discovery and Data Mining in Medicine / Chapter 7：
Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network / Chapter 8：
Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets / Chapter 9：
Index
Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
10.
ＥＢ
Amit Konar, L. C. Jain, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer London, 2005
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Foreword
Preface
Acknowledgments
The Psychological Basis of Cognitive Modeling / Chapter 1：
Introduction / 1.1：
Cognitive Models of Pattern Recognition / 1.2：
Template-Matching Theory / 1.2.1：
Prototype-Matching Theory / 1.2.2：
Feature-Based Approach for Pattern Recognition / 1.2.3：
The Computational Approach / 1.2.4：
Cognitive Models of Memory / 1.3：
Atkinson-Shiffrin's Model / 1.3.1：
Debates on Atkinson-Shiffrin's Model / 1.3.2：
Tulving's Model / 1.3.3：
The Parallel Distributed Processing Approach / 1.3.4：
Mental Imagery / 1.4：
Mental Representation of Imagery / 1.4.1：
Rotation of Mental Imagery / 1.4.2：
Imagery and Size / 1.4.3：
Imagery and Shape / 1.4.4：
Part-Whole Relationship in Mental Imagery / 1.4.5：
Ambiguity in Mental Imagery / 1.4.6：
Neurophysiological Similarity between Imagery and Perception / 1.4.7：
Cognitive Maps of Mental Imagery / 1.4.8：
Understanding a Problem / 1.5：
Steps in Understanding a Problem / 1.5.1：
A Cybernetic View of Cognition / 1.6：
The States of Cognition / 1.6.1：
Computational Modeling of Cognitive Systems / 1.7：
Petri Nets: A Brief Review / 1.8：
Extension of Petri Net Models for Distributed Modeling of Cognition / 1.9：
Scope of the Book / 1.10：
Summary / 1.11：
Exercises
References
Parallel and Distributed Logic Programming / Chapter 2：
Formal Definitions / 2.1：
Preliminary Definitions / 2.2.1：
Properties of the Substitution Set / 2.2.2：
SLD Resolution / 2.2.3：
Concurrency in Resolution / 2.3：
Types of Concurrent Resolution / 2.3.1：
Petri Net Model for Concurrent Resolution / 2.4：
Extended Petri Net / 2.4.1：
Algorithm for Concurrent Resolution / 2.4.2：
Performance Analysis of Petri Net-Based Models / 2.5：
The Speed-up / 2.5.1：
The Resource Utilization Rate / 2.5.2：
Resource Unlimited Speed-up and Utilization Rate / 2.5.3：
Conclusions / 2.6：
Distributed Reasoning by Fuzzy Petri Nets: A Review / Chapter 3：
Fuzzy Logic and Approximate Reasoning / 3.1：
Structured Models of Approximate Reasoning / 3.2：
Looney's Model / 3.3：
The Model Proposed by Chen et al / 3.4：
Konar and Mandal's Model / 3.5：
Yu's Model / 3.6：
Chen's Model for Backward Reasoning / 3.7：
Bugarin and Barro's Model / 3.8：
Pedrycz and Gomide's Learning Model / 3.9：
Construction of Reduction Rules Using FPN / 3.10：
Scope of Extension of Fuzzy Reasoning on Petri Nets / 3.11：
Belief Propagation and Belief Revision Models in Fuzzy Petri Nets / 3.12：
Imprecision Management in an Acyclic FPN / 4.1：
Formal Definitions and the Proposed Model / 4.2.1：
Proposed Model for Belief Propagation / 4.2.2：
Proposed Algorithm for Belief Propagation / 4.2.3：
Properties of FPN and Belief Propagation Scheme / 4.2.4：
Imprecision and Inconsistency Management in a Cyclic FPN / 4.3：
Proposed Model for Belief Revision / 4.3.1：
Stability Analysis of the Belief Revision Model / 4.3.2：
Detection and Elimination of Limit Cycles / 4.3.3：
Nonmonotonic Reasoning in an FPN / 4.3.4：
Building Expert Systems Using Fuzzy Petri Nets / 4.4：
The Database / 5.1：
The Data-tree / 5.2.1：
The Knowledge Base / 5.3：
The Inference Engine / 5.4：
Searching Antecedent Parts of PR in the Data-tree / 5.4.1：
Formation of the FPN / 5.4.2：
Decision Making and Explanation Tracing / 5.4.3：
A Case Study / 5.5：
Performance Evaluation / 5.6：
Time-Complexisty for the Default-Data-Tree-Formation Procedure / 5.6.1：
Time-Complexity for the Procedure Suspect-Identification / 5.6.2：
Time-Complexity for the Procedure Variable-Instantiation-of-PRs / 5.6.3：
Time-Complexity for the Procedure Create-tree / 5.6.4：
Time-Complexity for the Procedure Search-on-Data-Tree / 5.6.5：
Time-Complexity for the Procedure FPN-Formation / 5.6.6：
Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure / 5.6.7：
Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination / 5.6.8：
Time-Complexity for the Procedure Nonmonotonic Reasoning / 5.6.9：
Time-Complexity for the Procedure Decision-Making and Explanation Tracing / 5.6.10：
Time-Complexity of the Overall Expert System / 5.6.11：
Distributed Learning Using Fuzzy Cognitive Maps / 5.7：
Axelord's Cognitive Maps / 6.1：
Kosko's Model / 6.3：
Kosko's Extended Model / 6.4：
Adaptive FCMs / 6.5：
Zhang, Chen, and Bezdek's Model / 6.6：
Pal and Konar's FCM Model / 6.7：
Unsupervised Learning by Fuzzy Petri Nets / 6.8：
The Proposed Model for Cognitive Learning / 7.1：
Encoding of Weights / 7.2.1：
The Recall Model / 7.2.2：
State-Space Formulation / 7.3：
State-Space Model for Belief Updating / 7.3.1：
State-Space Model for FTT Updating of Transitions / 7.3.2：
State-Space Model for Weights / 7.3.3：
Stability Analysis of the Cognitive Model / 7.4：
Computer Simulation / 7.5：
Implication of the Results / 7.6：
Knowledge Refinement by Hebbian Learning / 7.7：
The Encoding Model / 7.7.1：
The Recall/Reasoning Model / 7.7.2：
Case Study by Computer Simulation / 7.7.3：
Supervised Learning by a Fuzzy Petri Net / 7.7.4：
Proposed Model of Fuzzy Petri Nets / 8.1：
Algorithm for Training / 8.2.1：
Analysis of Convergence / 8.4：
Application in Fuzzy Pattern Recognition / 8.5：
Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets / 8.6：
State-Space Formulation of the Proposed FPN Model / 9.1：
The Behavioral Model of FPN / 9.3.1：
State-Space Formulation of the Model / 9.3.2：
Special Cases of the Model / 9.3.3：
Stability Analysis / 9.4：
Forward Reasoning in FPNs / 9.5：
Abductive Reasoning in FPN / 9.6：
Bi-directional Reasoning in an FPN / 9.7：
Fuzzy Modus Tollens and Duality / 9.8：
Human Mood Detection and Control: A Cybernetic Approach / 9.9：
Filtering, Segmentation and Localization of Facial Components / 10.1：
Segmentation of the Mouth Region / 10.2.1：
Segmentation of the Eye Region / 10.2.2：
Segmentation of Eyebrow Constriction / 10.2.3：
Determination of Facial Attributes / 10.3：
Determination of the Mouth-Opening / 10.3.1：
Determination of the Eye-Opening / 10.3.2：
Determination of the Length of Eyebrow-Constriction / 10.3.3：
Fuzzy Relational Model for Mood Detection / 10.4：
Fuzzification of Facial Attributes / 10.4.1：
The Fuzzy Relational Model for Mood Detection / 10.4.2：
Validation of System Performance / 10.5：
A Basic Scheme of Human Mood Control / 10.6：
A Simple Model of Human Mood Transition Dynamics / 10.7：
The Model / 10.7.1：
Properties of the Model / 10.7.2：
The Proportional Model of Human Mood Control / 10.8：
Mamdani's Model for Mood Control / 10.9：
Ranking the Music, Audio, and Video Clips / 10.10：
Experimental Results / 10.11：
Distributed Planning and Multi-agent Coordination of Robots / 10.12：
Single-Agent Planning / 11.1：
Multi-Agent Planning / 11.3：
Task Sharing and Distribution in Multi-agent Planning / 11.3.1：
Cooperation with/without Communication / 11.3.2：
Homogeneous and Heterogeneous Distributed Planning / 11.3.3：
Vision-based Transportation of Blocks by Two Robots / 11.4：
Timing Analysis of the Transportation Problem / 11.5：
Analysis with Two agents / 11.6.1：
Analysis with /-agents / 11.6.2：
Index / 11.7：
Foreword
Preface
Acknowledgments
11.
ＥＢ
Ajith; Goldberg, Robert; Jain, Lakhmi C. Abraham, Ajith Abraham, Robert Goldberg, L. C. Jain, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer London, 2005
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12.
ＥＢ
Lakhmi Jain, Cosmin Danut Bocaniala, L. C. Jain, Vasile Palade, Xindong Wu
出版情報:
Springer eBooks Computer Science , Springer London, 2006
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Contributors
Foreword
Preface
Computational intelligence methodologies in fault diagnosis: Review and state of the art / Cosmin Danut Bocaniala ; Vasile Palade1：
A fuzzy logic approach to gas path diagnostics in aero-engines / Luca Marinai ; Riti Singh2：
Fault detection and isolation of industrial processes using optimized fuzzy models / Luis Mendonca ; Joao Sousa ; Jose Sa da Costa3：
A fuzzy classification technique applied to fault diagnosis / 4：
Fuzzy-statistical reasoning in fault diagnosis / Dan Stefanoiu ; Florin Ionescu5：
Artificial neural networks in fault diagnosis: A gas turbine scenario / Stephen Ogaji6：
Two-stage neural networks based classifier system for fault diagnosis / Arunas Lipnickas7：
Soft computing models for fault diagnosis of conductive flow systems / Viorel Ariton8：
Fault diagnosis in a power generation plant using a neural fuzzy system with rule extraction / Kok Yeng Chen ; Chee Peng Lim ; Weng Kin Lai9：
Fuzzy neural networks applied to fault diagnosis / Joao Calado10：
Causal models for distributed fault diagnosis of complex systems / 11：
Index
Contributors
Foreword
Preface
13.
ＥＢ
International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer Berlin / Heidelberg, 2005
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14.
ＥＢ
International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer Berlin / Heidelberg, 2005
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15.
ＥＢ
International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer Berlin / Heidelberg, 2005
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16.
ＥＢ
International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain
出版情報:
Springer eBooks Computer Science , Springer Berlin / Heidelberg, 2005
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17.
ＥＢ
Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, J?rg Siekmann, Societ? italiana reti neuroniche.
出版情報:
Springer eBooks Computer Science , Springer Berlin Heidelberg, 2007
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18.
ＥＢ
Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, J?rg Siekmann, Societ? italiana reti neuroniche.
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Springer eBooks Computer Science , Springer Berlin Heidelberg, 2007
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19.
ＥＢ
Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, J?rg Siekmann, Societ? italiana reti neuroniche.
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Springer eBooks Computer Science , Springer Berlin Heidelberg, 2007
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20.
図書
[editors, R.J. Howlett, L.C. Jain]
出版情報:
Piscataway, NJ : IEEE Service Center, c2000 2v. (877 p.) ; 30 cm
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21.
図書
Dawn E. Holmes, Lakhmi C. Jain (eds.)
22.
図書
Bogdan Gabrys, Robert J. Howlett, Lakhmi C. Jain (eds.)
23.
図書
Mika Sato-Ilic, Lakhmi C. Jain
目次情報:
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Introduction to Fuzzy Clustering
Fuzzy Clustering based Principal Component Analysis
Fuzzy Clustering based Regression Analysis
Kernel based Fuzzy Clustering
Evaluation of Fuzzy Clustering
Self-Organized Fuzzy Clustering
Introduction to Fuzzy Clustering
Fuzzy Clustering based Principal Component Analysis
Fuzzy Clustering based Regression Analysis
24.
図書
Rajiv Khosla, Robert J. Howlett, Lakhmi C. Jain (eds.)
25.
図書
Ashish Ghosh, Lakhmi C. Jain (eds.)
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Evolutionary Algorithms for Data Mining and Knowledge Discovery
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining
GAP: Constructing and Selecting Features with Evolutionary Computing
Multi-Agent Data Mining Using Evolutionary Computing
A Rule Extraction System with Class-Dependent Features
Knowledge Discovery in Data Mining via an Evolutionary Algorithm
Diversity and Neuro-Ensemble
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets
Evolutionary Computation in Intelligent Network Management
Genetic Programming in Data Mining for Drug Discovery
Microarray Data Mining with Evolutionary Computation
An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts
Evolutionary Algorithms for Data Mining and Knowledge Discovery
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining
GAP: Constructing and Selecting Features with Evolutionary Computing
26.
図書
Nikhil R. Pal and Lakhmi Jain (eds.)
目次情報:
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Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
Clustering and Visualization of Retail Market Baskets / Chapter 3：
Segmentation of Continuous Data Streams Based on a Change Detection Methodology / Chapter 4：
Instance Selection Using Evolutionary Algorithms: An Experimental Study / Chapter 5：
Using Cooperative Coevolution for Data Mining of Bayesian Networks / Chapter 6：
Knowledge Discovery and Data Mining in Medicine / Chapter 7：
Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network / Chapter 8：
Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets / Chapter 9：
Index
Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
27.
ＥＢ
Ajith; Goldberg, Robert; Jain, Lakhmi C. Abraham, Ajith Abraham, Robert Goldberg, L. C. Jain, Lakhmi C. Jain
出版情報:
SpringerLink Books - AutoHoldings , Springer London, 2005
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28.
ＥＢ
Amit Konar, L. C. Jain, Lakhmi C. Jain
出版情報:
SpringerLink Books - AutoHoldings , Springer London, 2005
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Foreword
Preface
Acknowledgments
The Psychological Basis of Cognitive Modeling / Chapter 1：
Introduction / 1.1：
Cognitive Models of Pattern Recognition / 1.2：
Template-Matching Theory / 1.2.1：
Prototype-Matching Theory / 1.2.2：
Feature-Based Approach for Pattern Recognition / 1.2.3：
The Computational Approach / 1.2.4：
Cognitive Models of Memory / 1.3：
Atkinson-Shiffrin's Model / 1.3.1：
Debates on Atkinson-Shiffrin's Model / 1.3.2：
Tulving's Model / 1.3.3：
The Parallel Distributed Processing Approach / 1.3.4：
Mental Imagery / 1.4：
Mental Representation of Imagery / 1.4.1：
Rotation of Mental Imagery / 1.4.2：
Imagery and Size / 1.4.3：
Imagery and Shape / 1.4.4：
Part-Whole Relationship in Mental Imagery / 1.4.5：
Ambiguity in Mental Imagery / 1.4.6：
Neurophysiological Similarity between Imagery and Perception / 1.4.7：
Cognitive Maps of Mental Imagery / 1.4.8：
Understanding a Problem / 1.5：
Steps in Understanding a Problem / 1.5.1：
A Cybernetic View of Cognition / 1.6：
The States of Cognition / 1.6.1：
Computational Modeling of Cognitive Systems / 1.7：
Petri Nets: A Brief Review / 1.8：
Extension of Petri Net Models for Distributed Modeling of Cognition / 1.9：
Scope of the Book / 1.10：
Summary / 1.11：
Exercises
References
Parallel and Distributed Logic Programming / Chapter 2：
Formal Definitions / 2.1：
Preliminary Definitions / 2.2.1：
Properties of the Substitution Set / 2.2.2：
SLD Resolution / 2.2.3：
Concurrency in Resolution / 2.3：
Types of Concurrent Resolution / 2.3.1：
Petri Net Model for Concurrent Resolution / 2.4：
Extended Petri Net / 2.4.1：
Algorithm for Concurrent Resolution / 2.4.2：
Performance Analysis of Petri Net-Based Models / 2.5：
The Speed-up / 2.5.1：
The Resource Utilization Rate / 2.5.2：
Resource Unlimited Speed-up and Utilization Rate / 2.5.3：
Conclusions / 2.6：
Distributed Reasoning by Fuzzy Petri Nets: A Review / Chapter 3：
Fuzzy Logic and Approximate Reasoning / 3.1：
Structured Models of Approximate Reasoning / 3.2：
Looney's Model / 3.3：
The Model Proposed by Chen et al / 3.4：
Konar and Mandal's Model / 3.5：
Yu's Model / 3.6：
Chen's Model for Backward Reasoning / 3.7：
Bugarin and Barro's Model / 3.8：
Pedrycz and Gomide's Learning Model / 3.9：
Construction of Reduction Rules Using FPN / 3.10：
Scope of Extension of Fuzzy Reasoning on Petri Nets / 3.11：
Belief Propagation and Belief Revision Models in Fuzzy Petri Nets / 3.12：
Imprecision Management in an Acyclic FPN / 4.1：
Formal Definitions and the Proposed Model / 4.2.1：
Proposed Model for Belief Propagation / 4.2.2：
Proposed Algorithm for Belief Propagation / 4.2.3：
Properties of FPN and Belief Propagation Scheme / 4.2.4：
Imprecision and Inconsistency Management in a Cyclic FPN / 4.3：
Proposed Model for Belief Revision / 4.3.1：
Stability Analysis of the Belief Revision Model / 4.3.2：
Detection and Elimination of Limit Cycles / 4.3.3：
Nonmonotonic Reasoning in an FPN / 4.3.4：
Building Expert Systems Using Fuzzy Petri Nets / 4.4：
The Database / 5.1：
The Data-tree / 5.2.1：
The Knowledge Base / 5.3：
The Inference Engine / 5.4：
Searching Antecedent Parts of PR in the Data-tree / 5.4.1：
Formation of the FPN / 5.4.2：
Decision Making and Explanation Tracing / 5.4.3：
A Case Study / 5.5：
Performance Evaluation / 5.6：
Time-Complexisty for the Default-Data-Tree-Formation Procedure / 5.6.1：
Time-Complexity for the Procedure Suspect-Identification / 5.6.2：
Time-Complexity for the Procedure Variable-Instantiation-of-PRs / 5.6.3：
Time-Complexity for the Procedure Create-tree / 5.6.4：
Time-Complexity for the Procedure Search-on-Data-Tree / 5.6.5：
Time-Complexity for the Procedure FPN-Formation / 5.6.6：
Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure / 5.6.7：
Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination / 5.6.8：
Time-Complexity for the Procedure Nonmonotonic Reasoning / 5.6.9：
Time-Complexity for the Procedure Decision-Making and Explanation Tracing / 5.6.10：
Time-Complexity of the Overall Expert System / 5.6.11：
Distributed Learning Using Fuzzy Cognitive Maps / 5.7：
Axelord's Cognitive Maps / 6.1：
Kosko's Model / 6.3：
Kosko's Extended Model / 6.4：
Adaptive FCMs / 6.5：
Zhang, Chen, and Bezdek's Model / 6.6：
Pal and Konar's FCM Model / 6.7：
Unsupervised Learning by Fuzzy Petri Nets / 6.8：
The Proposed Model for Cognitive Learning / 7.1：
Encoding of Weights / 7.2.1：
The Recall Model / 7.2.2：
State-Space Formulation / 7.3：
State-Space Model for Belief Updating / 7.3.1：
State-Space Model for FTT Updating of Transitions / 7.3.2：
State-Space Model for Weights / 7.3.3：
Stability Analysis of the Cognitive Model / 7.4：
Computer Simulation / 7.5：
Implication of the Results / 7.6：
Knowledge Refinement by Hebbian Learning / 7.7：
The Encoding Model / 7.7.1：
The Recall/Reasoning Model / 7.7.2：
Case Study by Computer Simulation / 7.7.3：
Supervised Learning by a Fuzzy Petri Net / 7.7.4：
Proposed Model of Fuzzy Petri Nets / 8.1：
Algorithm for Training / 8.2.1：
Analysis of Convergence / 8.4：
Application in Fuzzy Pattern Recognition / 8.5：
Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets / 8.6：
State-Space Formulation of the Proposed FPN Model / 9.1：
The Behavioral Model of FPN / 9.3.1：
State-Space Formulation of the Model / 9.3.2：
Special Cases of the Model / 9.3.3：
Stability Analysis / 9.4：
Forward Reasoning in FPNs / 9.5：
Abductive Reasoning in FPN / 9.6：
Bi-directional Reasoning in an FPN / 9.7：
Fuzzy Modus Tollens and Duality / 9.8：
Human Mood Detection and Control: A Cybernetic Approach / 9.9：
Filtering, Segmentation and Localization of Facial Components / 10.1：
Segmentation of the Mouth Region / 10.2.1：
Segmentation of the Eye Region / 10.2.2：
Segmentation of Eyebrow Constriction / 10.2.3：
Determination of Facial Attributes / 10.3：
Determination of the Mouth-Opening / 10.3.1：
Determination of the Eye-Opening / 10.3.2：
Determination of the Length of Eyebrow-Constriction / 10.3.3：
Fuzzy Relational Model for Mood Detection / 10.4：
Fuzzification of Facial Attributes / 10.4.1：
The Fuzzy Relational Model for Mood Detection / 10.4.2：
Validation of System Performance / 10.5：
A Basic Scheme of Human Mood Control / 10.6：
A Simple Model of Human Mood Transition Dynamics / 10.7：
The Model / 10.7.1：
Properties of the Model / 10.7.2：
The Proportional Model of Human Mood Control / 10.8：
Mamdani's Model for Mood Control / 10.9：
Ranking the Music, Audio, and Video Clips / 10.10：
Experimental Results / 10.11：
Distributed Planning and Multi-agent Coordination of Robots / 10.12：
Single-Agent Planning / 11.1：
Multi-Agent Planning / 11.3：
Task Sharing and Distribution in Multi-agent Planning / 11.3.1：
Cooperation with/without Communication / 11.3.2：
Homogeneous and Heterogeneous Distributed Planning / 11.3.3：
Vision-based Transportation of Blocks by Two Robots / 11.4：
Timing Analysis of the Transportation Problem / 11.5：
Analysis with Two agents / 11.6.1：
Analysis with /-agents / 11.6.2：
Index / 11.7：
Foreword
Preface
Acknowledgments
29.
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Lakhmi Jain, Cosmin Danut Bocaniala, L. C. Jain, Vasile Palade, Xindong Wu, Lakhmi C. Jain
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Contributors
Foreword
Preface
Computational intelligence methodologies in fault diagnosis: Review and state of the art / Cosmin Danut Bocaniala ; Vasile Palade1：
A fuzzy logic approach to gas path diagnostics in aero-engines / Luca Marinai ; Riti Singh2：
Fault detection and isolation of industrial processes using optimized fuzzy models / Luis Mendonca ; Joao Sousa ; Jose Sa da Costa3：
A fuzzy classification technique applied to fault diagnosis / 4：
Fuzzy-statistical reasoning in fault diagnosis / Dan Stefanoiu ; Florin Ionescu5：
Artificial neural networks in fault diagnosis: A gas turbine scenario / Stephen Ogaji6：
Two-stage neural networks based classifier system for fault diagnosis / Arunas Lipnickas7：
Soft computing models for fault diagnosis of conductive flow systems / Viorel Ariton8：
Fault diagnosis in a power generation plant using a neural fuzzy system with rule extraction / Kok Yeng Chen ; Chee Peng Lim ; Weng Kin Lai9：
Fuzzy neural networks applied to fault diagnosis / Joao Calado10：
Causal models for distributed fault diagnosis of complex systems / 11：
Index
Contributors
Foreword
Preface
30.
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Lakhmi C. Jain, L. C. Jain, Nikhil R. Pal, Xindong Wu, Nikhil Pal
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Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
Clustering and Visualization of Retail Market Baskets / Chapter 3：
Segmentation of Continuous Data Streams Based on a Change Detection Methodology / Chapter 4：
Instance Selection Using Evolutionary Algorithms: An Experimental Study / Chapter 5：
Using Cooperative Coevolution for Data Mining of Bayesian Networks / Chapter 6：
Knowledge Discovery and Data Mining in Medicine / Chapter 7：
Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network / Chapter 8：
Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets / Chapter 9：
Index
Preface
Trends in Data Mining and Knowledge Discovery / Chapter 1：
Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data / Chapter 2：
31.
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International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Robert J. Howlett, L. C. Jain, Mircea Gh Negoita
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32.
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International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Robert J. Howlett, L. C. Jain, Mircea Gh Negoita
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33.
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International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Robert J. Howlett, L. C. Jain, Mircea Gh Negoita
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34.
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International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain, Josef Kittler
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35.
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International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain, Josef Kittler
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36.
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International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain, Josef Kittler
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37.
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International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Takeo Kanade, Rajiv Khosla, Lakhmi C. Jain, Josef Kittler
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38.
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International Conference on Knowledge-Based Intelligent Engineering Systems, Robert J. Howlett, L. C. Jain, Vasile Palade, Lakhmi C. Jain
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39.
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Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade, Josef Kittler
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40.
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Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade, Josef Kittler
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41.
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Bogdan Gabrys, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Takeo Kanade, Josef Kittler
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42.
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Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, Jörg Siekmann, Società italiana reti neuroniche.
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43.
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Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, Jörg Siekmann, Società italiana reti neuroniche.
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44.
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Bruno Apolloni, Robert J. Howlett, L. C. Jain, Lakhmi Jain, J. Siekmann, Jörg Siekmann, Società italiana reti neuroniche.
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45.
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Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Jörg Siekmann
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46.
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Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Jörg Siekmann
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47.
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Ignac Lovrek, Robert J. Howlett, L. C. Jain, Lakhmi C. Jain, Jörg Siekmann
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図書
ＥＢ
