The handbook on reasoning-based intelligent systems / edited by Kazumi Nakamatsu, Lakhmi C. Jain.

Format:

Book

Contributor:

Nakamatsu, Kazumi.

Jain, L. C.

Language:

English

Subjects (All):

Intelligent control systems.

Artificial intelligence.

Physical Description:

1 online resource (680 p.)

Place of Publication:

Singapore ; Hackensack, NJ : World Scientific, 2013.

Language Note:

English

Summary:

This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.

Contents:

CONTENTS; 6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function

2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. Some Studies on Data Mining Dilip Kumar Pratihar; 3.1 Introduction

3.2 Classification Tools3.3 Statistical Regression Analysis; 3.3.1 Design of experiments; 3.3.1.1 Full-factorial design of experiments; 3.3.1.2 Central composite design of experiments; 3.3.2 Regression analysis; 3.3.2.1 Linear regression analysis; 3.3.2.2 Non-linear regression analysis; 3.3.3 Adequacy of the model; 3.3.4 Drawbacks; 3.4 Dimensionality Reduction Techniques; 3.4.1 Sammon's Non-linear Mapping (Sammon, 1969); 3.4.2 VISOR Algorithm (Konig, 1994); 3.4.3 Self-organizing map (Kohenen, 1995); 3.4.4 GA-like approach (Dutta and Pratihar, 2006); 3.4.5 Comparisons

3.4.6 Dimensionality reduction approaches for large data sets3.5 Clustering Techniques; 3.5.1 Fuzzy C-means algorithm (Bezdek, 1973); 3.5.2 Entropy-based fuzzy clustering (Yao et al., 2000); 3.5.3 Comparisons; 3.5.4 Clustering of large spatial data sets; 3.6 Cluster-wise Regression Analysis; 3.7 Intelligent Data Mining; 3.8 Summary; Acknowledgement; References; 4. Rough Non-deterministic Information Analysis for Uncertain Information Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata; 4.1 Introduction; 4.2 An Overview of RNIA; 4.2.1 Basic Definitions; 4.2.2 Two Modalities in RNIA

4.2.3 Properties and Obtained Results in RNIA4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation); 4.3.1 Certain Rule Generation by the Order of Attributes; 4.3.2 Minimal Certain Rules; 4.3.3 Discernibility Functions and Minimal Certain Rule Generation; 4.3.4 Enumeration Method for Obtaining Minimal Solutions; 4.3.5 Interactive Selection Method for Obtaining Minimal Solutions; 4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions; 4.3.7 Programs for ISETV-method

4.3.8 Possible Rule Generation

Notes:

Description based upon print version of record.

Includes bibliographical references.

ISBN:

1-299-28128-1

981-4329-48-7

OCLC:

830162006