Soft Computing and Human-Centered Machines

Overview

Today's networked world and the decentralization that the Web enables and symbolizes have created new phenomena: information explosion and saturation. To deal with information overload, our computers should have human-centered functionality and enhanced intelligence, but instead they simply become faster. Soft computing is a unifying framework that combines techniques in neural networks, fuzzy theory, genetic algorithms, and artificial intelligence to develop intelligent systems able to learn in dynamic, ...

See more details below
Paperback (Softcover reprint of the original 1st ed. 2000)
$84.00
BN.com price
(Save 15%)$99.00 List Price
Other sellers (Paperback)
  • All (3) from $92.16   
  • New (2) from $92.16   
  • Used (1) from $141.16   
Sending request ...

Overview

Today's networked world and the decentralization that the Web enables and symbolizes have created new phenomena: information explosion and saturation. To deal with information overload, our computers should have human-centered functionality and enhanced intelligence, but instead they simply become faster. Soft computing is a unifying framework that combines techniques in neural networks, fuzzy theory, genetic algorithms, and artificial intelligence to develop intelligent systems able to learn in dynamic, imprecise, and uncertain environments. This book explains the theory, methodology, and application aspects of human-centered systems, showing how it is possible to extend to machines such techniques as dynamic cognitive learning, neural-fuzzy-based learning, and genetic-evolutionary type learning paradigms.

Read More Show Less

Product Details

  • ISBN-13: 9784431679868
  • Publisher: Springer Japan
  • Publication date: 12/31/2013
  • Series: Computer Science Workbench Series
  • Edition description: Softcover reprint of the original 1st ed. 2000
  • Pages: 327
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.73 (d)

Table of Contents

1 Introduction.- 1.1 The Third Industrial Revolution: human-centered machines.- 1.2 Soft Computing: a unifying framework for intelligent systems.- 2 Multisets and Fuzzy Multisets.- 2.1 Introduction.- 2.2 Multisets.- 2.3 Fuzzy Multisets.- 2.3.1 Basic Operations of Fuzzy Multisets.- 2.4 Infinite Fuzzy Multisets.- 2.4.1 Infinite Sequence of Memberships and Computability.- 2.4.2 Operations for Infinite Fuzzy Multisets.- 2.5 Another Ftizzification.- 2.6 Application to Query Language for Fuzzy Database.- 2.6.1 Fuzzy Multirelations.- 2.6.2 Functions in Fuzzy SQL.- 2.7 Conclusion.- 2.8 References.- 3 Modal Logic, Rough Sets, and Fuzzy Sets.- 3.1 Introduction.- 3.2 Language for Modal Logic.- 3.3 Kripke Semantics for Modal Logic.- 3.4 Truth Sets and Generalized Lower and Upper Approximations.- 3.5 Validity.- 3.6 What is a System of Modal Logic?.- 3.7 Normal Systems of Modal Logic.- 3.8 Soundness.- 3.9 Completeness.- 3.10 Fuzzy Sets and Rough Sets.- 3.11 Concluding Remarks.- 3.12 References.- 4 Fuzzy Cognitive Maps: Analysis and Extensions.- 4.1 Introduction.- 4.2 Fuzzy Cognitive Maps.- 4.2.1 Causality and Logical Implication.- 4.2.2 Building Fuzzy Cognitive Maps.- 4.2.3 Causal Inference in FCM.- 4.2.4 Combining Fuzzy Cognitive Maps.- 4.3 Extensions to FCM.- 4.3.1 FCM with Non-linear Edge Functions.- 4.3.2 FCM with Constant Time-Delays.- 4.3.3 Weighted Combination of FCMs.- 4.4 Analysis of Fuzzy Cognitive Maps.- 4.4.1 FCM and Its State Space.- 4.4.2 Causal Module of FCM.- 4.4.3 Inference Patterns of Basic FCMs.- 4.4.4 Inference Pattern of General FCMs.- 4.5 Conclusions.- 4.6 References.- 5 Methods in Hard and Fuzzy Clustering.- 5.1 Introduction.- 5.2 Basic Methods in Clustering.- 5.3 Fuzzy c-Means.- 5.4 Other Nonhierarchical Methods.- 5.5 A Numerical Example.- 5.6 Fuzzy Hierarchical Clustering.- 5.7 Conclusions.- 5.8 References.- 6 Soft-Competitive Learning Paradigms.- 6.1 Introduction.- 6.2 Learning by Neural Networks.- 6.2.1 Supervised Learning.- 6.2.2 Unsupervised Learning.- 6.2.3 Reinforcement Learning.- 6.3 Competitive Learning Paradigm.- 6.3.1 Classic Competitive Learning.- 6.4 Overview of Competitive Learning Schemes.- 6.4.1 Winner-Take-Most (WTM) Paradigm.- 6.4.2 Competitive Learning with Conscience.- 6.4.3 Penalizing in Competitive Learning.- 6.4.4 Learning Schemes with Variable Number of Prototypes.- 6.4.5 Fuzzy Clustering Algorithms.- 6.5 Fuzzy Competitive Learning and Soft Competition.- 6.5.1 Conscience and Frequency Sensitive Competitive Learning.- 6.5.2 Rival Penalized Competitive Learning.- 6.6 Compensated Competitive Learning.- 6.6.1 The Concept of Compensated Competitive Learning.- 6.6.2 Varying the Number of Penalized Vectors in CCL.- 6.7 Conclusions.- 6.8 References.- 7 Aggregation Operations for Fusing Fuzzy Information.- 7.1 Introduction.- 7.2 Intersection and Union of Fuzzy Sets.- 7.3 Weighted Unions and Intersections.- 7.4 Uninorms.- 7.5 Mean Aggregation Operators.- 7.6 Ordered Weighted Averaging Operators.- 7.7 Linguistic Quantifiers and OWA Operators.- 7.8 Aggregation Using Fuzzy Measures.- 7.9 Conclusion.- 7.10 References.- 8 Fuzzy Gated Neural Networks in Pattern Recognition.- 8.1 Introduction.- 8.2 Generalized Gated Neuron Model.- 8.3 Fuzzy Gated Neural Networks.- 8.3.1 System Structure.- 8.3.2 Input, Gate, and Output Functions.- 8.3.3 Temporal Aggregation.- 8.4 Comparison between FGNN and STFM.- 8.4.1 FGNN’s Operational Characteristics.- 8.5 Experimental Results.- 8.5.1 2D Real World Texture Data.- 8.5.2 3D Synthetic Images.- 8.5.3 Real Range Images.- 8.5.4 Results and Discussions.- 8.6 Improvements to FGNN.- 8.6.1 Performance under Noisy Data.- 8.6.2 Noise Cover in FGNN.- 8.6.3 Knowledge Acquisition and Aggregation.- 8.7 The Improved FGNN.- 8.7.1 Mean and Bayesian Aggregation Methods.- 8.7.2 Alternative Aggregation Methods.- 8.8 Conclusions.- 8.9 References.- 9 Soft Computing Technique in Kansei (Emotional) Information Processing.- 9.1 Introduction.- 9.2 Concept of Kansei Information.- 9.2.1 Difference Between Intelligent Information and Kansei Information.- 9.2.2 Kansei Information from Soft Computing Approaches.- 9.2.3 Facial Expressions as Kansei Information.- 9.3 Study Examples of Facial Expressions.- 9.3.1 Recognition Model of Emotions Through Facial Expressions Considering Situations.- 9.3.2 Application of Recognition Model of Emotions Through Facial Expressions Considering Situations.- 9.3.3 Facial Caricature Drawing.- 9.4 Conclusions.- 9.5 References.- 10 Vagueness in Human Judgment and Decision Making.- 10.1 Introduction.- 10.2 Theoretical Representation of Vagueness in Judgment and Decision Making.- 10.3 Measurement and Fuzzy-Set Representation of Vagueness in Judgment and Decision Making.- 10.4 Experimental Studies of Vagueness of Judgment and Decision Making Using the Fuzzy Rating Method.- 10.5 Regression Analyses for Fuzzy Rating Data.- 10.6 Conclusion.- 10.7 References.- 11 Chaos and Time Series Analysis.- 11.1 Introduction.- 11.2 Embedding Time Series Data.- 11.3 Deterministic Nonlinear Prediction.- 11.4 Analysis of Complicated Time Series by Deterministic Nonlinear Prediction.- 11.5 Engineering Applications of Deterministic Nonlinear Prediction.- 11.6 Chaotic Time Series Analysis and Statistical Hypothesis Testing.- 11.7 Conclusions.- 11.8 References.- 12 A Short Course for Fuzzy Set Theory.- 12.1 Classical Sets.- 12.2 Fuzzy Sets.- 12.3 Basic Operations on Fuzzy Sets.- 12.4 Extension Principle.- 12.5 Fuzzy Relations.- 12.6 Possibility and Necessity Measures.- 12.7 Fuzzy Numbers.- 12.8 Discussion and Remarks.- 12.9 References.

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)