In this paper for the first time two new fuzzy models viz Merged Fuzzy Cognitive Maps (MFCMs) models and Specially Merged Linked Fuzzy Cognitive Maps (SMLFCMs) are introduced. To compare the experts opinion a new techniques called Kosko Hamming distance and Kosko Hamming weight are introduced.

This book presents the proceedings of the International Science and Technology Conference “FarEastCon 2019,” which took place on October 1–4, 2019, in Vladivostok, Russian Federation. The conference provided a platform for gathering expert opinions on projects and initiatives aimed at the implementation of far-sighted scientific research and development, and allowed current theoretical and practical advances to be shared with the broader research community. Featuring selected papers from the conference, this book will be of interest to experts in various fields whose work involves developing innovative solutions and increasing the efficiency of economic activities.

In this book authors for the first time introduce the notion of distance between any two m x n matrices. If the distance is 0 or m x n there is nothing interesting.

In this book the authors for the first time have merged vertices and edges of lattices to get a new structure which may or may not be a lattice but is always a graph. This merging is done for graph too which will be used in the merging of fuzzy models. Further merging of graphs leads to the merging of matrices; both these concepts play a vital role in merging the fuzzy and neutrosophic models. Several open conjectures are suggested.

This book provides recent research on soft computing and fuzzy methodologies in innovation management and sustainability. The uncertainty in the business world is increasing. Significant changes are generated unexpectedly, so using fuzzy logic and soft computing methods allows us to create flexible scenarios adaptable to new realities. Within the book, we will find different applications of fuzzy methodologies that can apply to various topics such as sustainability, innovation, tourism, costs, exports, systems administration, among others. The book's main contribution is the applicability of the various methodologies to specific cases, which allows generating a relationship between theory and practice. In addition, it has some bibliometric studies on various topics that give us a visualization of what has happened and where multiple topics are headed. This book is recommended mainly for students who wish to know how the various fuzzy and soft computing tools can be taken to real situations, allowing a better understanding of these and generating new visions of future applicability.

In Part I, the impact of an integro-differential operator on parity logic engines (PLEs) as a tool for scientific modeling from scratch is presented. Part II outlines the fuzzy structural modeling approach for building new linear and nonlinear dynamical causal forecasting systems in terms of fuzzy cognitive maps (FCMs). Part III introduces the new type of autogenetic algorithms (AGAs) to the field of evolutionary computing. Altogether, these PLEs, FCMs, and AGAs may serve as conceptual and computational power tools.

Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.