Fuzzy Logic has gained increasing acceptance as a way to deal with complexity and uncertainty in many areas of science and engineering. This book is the first to address its practical applications to chemical systems. Ten distinguished authors discuss the role of fuzzy logic in the characterization of a variety of chemical concepts, including chirality, quantum systems, molecular engineering and design, and hierarchical classification methods. Fuzzy Logic in Chemistry will appeal to both students and professionals who are seeking to learn more about theory and applications in an area of growing importance to the physical sciences. - The first book on the applications of fuzzy logic in chemistry - Covers a topic relevant to many disciplines, including molecular design - Discusses applications of fuzzy logic to the physical sciences, a rapidly growing area - Features chapters from highly distinguished authors in the physical sciences

This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh’s seminal paper on “fuzzy sets,” published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh’s paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years’ anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments of Fuzzy Logic-based systems.

The term "fuzzy logic," as it is understood in this book, stands for all aspects of representing and manipulating knowledge based on the rejection of the most fundamental principle of classical logic---the principle of bivalence. According to this principle, each declarative sentence is required to be either true or false. In fuzzy logic, these classical truth values are not abandoned. However, additional, intermediate truth values between true and false are allowed, which are interpreted as degrees of truth. This opens a new way of thinking---thinking in terms of degrees rather than absolutes. For example, it leads to the definition of a new kind of sets, referred to as fuzzy sets, in which membership is a matter of degree. The book examines the genesis and development of fuzzy logic. It surveys the prehistory of fuzzy logic and inspects circumstances that eventually lead to the emergence of fuzzy logic. The book explores in detail the development of propositional, predicate, and other calculi that admit degrees of truth, which are known as fuzzy logic in the narrow sense. Fuzzy logic in the broad sense, whose primary aim is to utilize degrees of truth for emulating common-sense human reasoning in natural language, is scrutinized as well. The book also examines principles for developing mathematics based on fuzzy logic and provides overviews of areas in which this has been done most effectively. It also presents a detailed survey of established and prospective applications of fuzzy logic in various areas of human affairs, and provides an assessment of the significance of fuzzy logic as a new paradigm.

Expert systems allow scientists to access, manage, and apply data and specialized knowledge from various disciplines to their own research. Expert Systems in Chemistry Research explains the general scientific basis and computational principles behind expert systems and demonstrates how they can improve the efficiency of scientific workflows

Although human intelligence is deeply investigated by neuroscientists, psychologists, philosophers, and AI researchers, we still lack of a widely accepted definition of what it is. If we exploit the emergence theory from Complexity Science to give a definition, we might state that human intelligence is the emergent property of the human nervous system. Such fascinating emergent property allows us to handle both accurate and vague information by computing with numbers and words. Moreover, it allows us to reason, speak and take rational decisions in an environment of uncertainty, partiality and relativity of truth, when the “Incompatibility Principle” holds: “As the complexity of a system increases, accuracy and significance become almost mutually exclusive characteristics of our statements”. Finally, our intelligence allows us recognizing quite easily variable patterns. Therefore, it is worthwhile investigating human intelligence and trying to mimic it by developing Artificial Intelligence. Nowadays, Artificial Intelligence is in vogue: it is applied in both basic and applied science. Traditionally, there are two strategies to develop Artificial Intelligence. A strategy consists in writing human-like intelligent software running in von Neumann computers or special-purpose hardware. The other strategy consists in neuromorphic engineering. Neuromorphic engineering implements surrogates of neurons through non-biological systems, either for neuro-prosthesis or to design brain-like computing machines. A third strategy is now blooming and it consists in using molecular, supramolecular, materials, and systems chemistry to mimic some basic functions of human intelligence such as Boolean, multi-valued logic gates, and Fuzzy logic. This third strategy is originating Chemical Artificial Intelligence (CAI). A relevant purpose of CAI is to design modules for Chemical Robots. A Chemical Robot is thought of as a molecular assembly that reacts autonomously to its environment by probing it with molecular sensors, making decisions by its intrinsic Artificial Neural Networks or logic gates, and performing actions upon its environment through molecular effectors. The intelligent activities of any Chemical Robot should be sustained energetically by a metabolic unit. Chemical Robots should be easily miniaturized and implanted in living beings to interplay with cells or organelles for biomedical applications. They should become auxiliary elements of the natural immune system.

What is fuzzy logic?--a system of concepts and methods for exploring modes of reasoning that are approximate rather than exact. While the engineering community has appreciated the advances in understanding using fuzzy logic for quite some time, fuzzy logic's impact in non-engineering disciplines is only now being recognized. The authors of Fuzzy Logic in Geology attend to this growing interest in the subject and introduce the use of fuzzy set theory in a style geoscientists can understand. This is followed by individual chapters on topics relevant to earth scientists: sediment modeling, fracture detection, reservoir characterization, clustering in geophysical data analysis, ground water movement, and time series analysis.George Klir is the Distinguished Professor of Systems Science and Director of the Center for Intelligent Systems, Fellow of the IEEE and IFSA, editor of nine volumes, editorial board member of 18 journals, and author or co-author of 16 booksForeword by the inventor of fuzzy logic-- Professor Lotfi Zadeh

This edited volume contains ten papers on the subject of fuzzy technology. Fuzzy technology emerged as a combination of fuzzy sets theory, fuzzy logic and fuzzy-based reasoning. As a technology it gained a very practical meaning through thousands of applications in different theoretical as well as practical disciplines, covering mathematics, physics, chemistry, biology, life science, social science, economy, computer science, and (foremost) electrical, electronic, mechanical, nuclear, chemical, textile, aeronautic, ocean, and many other engineering disciplines. The goal of this book is to create an interest in fuzzy technology among researchers, engineers, professionals and students involved in the research and development in the broad area of artificial intelligence. This book is also intended to bring the reader up-to-date in the area of implementations and applications of fuzzy technology, as well as to generate and stimulate new research ideas in this area. It may inspire and motivate the researcher in new directions, as well as creating a force for new efforts to make a fuzzy technology commonly known and used in science and engineering. This volume appears at a time of unprecedented research interest in the field of fuzzy technology. I intentionally wrote research due to the events that have occurred during the last couple of years. To be more specific, I should describe this interest geographically.

There are many uncertainties in the real world. Fuzzy theory treats a kind of uncertainty called fuzziness, where it shows that the boundary of yes or no is ambiguous and appears in the meaning of words or is included in the subjunctives or recognition of human beings. Fuzzy theory is essential and is applicable to many systems -- from consumer products like washing machines or refrigerators to big systems like trains or subways. Recently, fuzzy theory has been a strong tool for combining new theories (called soft computing) such as genetic algorithms or neural networks to get knowledge from real data. This introductory book enables the reader to understand easily what fuzziness is and how one can apply fuzzy theory to real problems -- which explains why it was a best-seller in Japan.

The contributions to this book cover a wide range of applications of Soft Computing to the chemical domain. The early roots of Soft Computing can be traced back to Lotfi Zadeh's work on soft data analysis [1] published in 1981. 'Soft Computing' itself became fully established about 10 years later, when the Berkeley Initiative in Soft Computing (SISC), an industrial liaison program, was put in place at the University of California - Berkeley. Soft Computing applications are characterized by their ability to: • approximate many different kinds of real-world systems; • tolerate imprecision, partial truth, and uncertainty; and • learn from their environment. Such characteristics commonly lead to a better ability to match reality than other approaches can provide, generating solutions of low cost, high robustness, and tractability. Zadeh has argued that soft computing provides a solid foundation for the conception, design, and application of intelligent systems employing its methodologies symbiotically rather than in isolation. There exists an implicit commitment to take advantage of the fusion of the various methodologies, since such a fusion can lead to combinations that may provide performance well beyond that offered by any single technique.

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.