It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its...

From July 10th through July 13th, 1994, an informal workshop co-organized by RILEM committees 116-PCD and 123-MME was held at Saint-Remy-Ies Chevreuse, France, and attended by 38 delegates from 16 countries. Twenty-nine papers were presented, converging the general subjects of modelling micro structures and predicting durability of concrete and other cement-based materials. A short summary follows: G. M. Idom's paper entitled "Modelling Research for Concrete Engineering" serves as an introduction to the workshop, presenting an overview of modelling research with the conelusion that the broad...

Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for...

Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of...

Advances in Computational Intelligence and Learning: Methods and Applications presents new developments and applications in the area of Computational Intelligence, which essentially describes methods and approaches that mimic biologically intelligent behavior in order to solve problems that have been difficult to solve by classical mathematics. Generally Fuzzy Technology, Artificial Neural Nets and Evolutionary Computing are considered to be such approaches.

The Editors have assembled new contributions in the areas of fuzzy sets, neural sets and machine learning, as...

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty...

Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design, etc. Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic...

When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using "classical" mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made by the analyst when forming the mathematical model, the linguistic information is often ignored. On the other hand, a wide range of traffic and transportation engineering parameters are characterized by uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective knowledge...

5 TABLE I Average Reading Proficiency and Achievement Levels by RacelEthnicity Grades 4 8 and 12 1992 Reading Assessment Percentage of Student At or Above Percentage of Average Proficient Basic Below Basic Advanced I Students Proficiency I Grade 4 White 71 226 6 31 68 J2 69 16 193 0 Black 7 31 Hispanic 9 202 2 13 41 59 Asian/Pacific Islander 2 216 2 21 55 45 American Indian 2 208 2 15 50 50 Grade 8 70 White 268 3 34 77 23 Black 16 238 0 8 44 56 Hispanic 10 242 I 13 49 51 AsianlPacific Islander 3 270 6 38 77 23 American Indian 1 251 I 18 60 40 Grade 12 White 72 297 4 43 82 18 Black 15 272 0 16...

Digital Timing Macromodeling for VLSI Design Verification first of all provides an extensive history of the development of simulation techniques. It presents detailed discussion of the various techniques implemented in circuit, timing, fast-timing, switch-level timing, switch-level, and gate-level simulation. It also discusses mixed-mode simulation and interconnection analysis methods. The review in Chapter 2 gives an understanding of the advantages and disadvantages of the many techniques applied in modern digital macromodels.
The book also presents a wide variety of...