Donald C. Wunsch II, PhD, is the M.K. Finley Missouri Distinguished Professor at Missouri University of Science and Technology. His key contributions are in adaptive resonance and reinforcement learning hardware and applications, neurofuzzy regression, improved Traveling Salesman Problem heuristics, clustering, and bioinformatics. He is an IEEE Fellow, the 2005 International Neural Networks Society (INNS) President, and Senior Fellow of the INNS.
There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications.
Explains the following fundamental approaches for developing evolving intelligent systems (EIS):the Hierarchical Prioritized Structure
the Participatory Learning Paradigm
the Evolving Takagi-Sugeno fuzzy systems (eTS+)
the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm
Emphasizes the importance and increased interest in online processing of data streams
Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation
Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems
Introduces an integrated approach to incremental (real-time) feature extraction and classification
Proposes a study on the stability of evolving neuro-fuzzy recurrent networks
Details methodologies for evolving clustering and classification
Reveals different applications of EIS to address real problems in areas of:
evolving inferential sensors in chemical and petrochemical industry
learning and recognition in robotics
Features downloadable software resources
Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
Lotfi Zadeh, the father of fuzzy logic, coined the phrase "computing with words" (CWW) to describe a methodology in which the objects of computation are words and propositions drawn from a natural language. Perceptual Computing explains how to implement CWW to aid in the important area of making subjective judgments, using a methodology that leads to an interactive device—a "Perceptual Computer"—that propagates random and linguistic uncertainties into the subjective judgment in a way that can be modeled and observed by the judgment maker.
This book focuses on the three components of a Perceptual Computer—encoder, CWW engines, and decoder—and then provides detailed applications for each. It uses interval type-2 fuzzy sets (IT2 FSs) and fuzzy logic as the mathematical vehicle for perceptual computing, because such fuzzy sets can model first-order linguistic uncertainties whereas the usual kind of fuzzy sets cannot. Drawing upon the work on subjective judgments that Jerry Mendel and his students completed over the past decade, Perceptual Computing shows readers how to:
Map word-data with its inherent uncertainties into an IT2 FS that captures these uncertainties
Use uncertainty measures to quantify linguistic uncertainties
Compare IT2 FSs by using similarity and rank
Compute the subsethood of one IT2 FS in another such set
Aggregate disparate data, ranging from numbers to uniformly weighted intervals to nonuniformly weighted intervals to words
Aggregate multiple-fired IF-THEN rules so that the integrity of word IT2 FS models is preserved
Free MATLAB-based software is also available online so readers can apply the methodology of perceptual computing immediately, and even try to improve upon it. Perceptual Computing is an important go-to for researchers and students in the fields of artificial intelligence and fuzzy logic, as well as for operations researchers, decision makers, psychologists, computer scientists, and computational intelligence experts.