Preface

Abstract

This volume describes the solution of problems of Big Data intellectual analysis, control, design and optimization. Big Data - technologies that extract maximum benefit from big data, which are widespread in all spheres.

Starting with an official introduction to the basics of algorithm hybridization, this book combines many different aspects of current research on hybrid technologies, such as deep neural networks, fuzzy neural networks, multi-MISO ANFIS, fuzzy C-means, conditional disentangled networks, generative adversarial networks, finite difference method and enthalpy method.

The book also covers a wide range of applications and implementation problems, from pattern recognition and image generation to intelligent forecasting problems, automation of production in technical applications (3D-analysis, forecasting of distributed photovoltaic systems and loads) with due attention to modeling.

It covers a wide range of applications in the field of Big Data analysis, as well as Data Mining.

In addition to the traditional tasks of classification, clustering, forecasting, it also discusses original approaches to hybrid optimization and control in the tasks of multi-object optimization for smart grid, natural gas hydrate wellbore, parallel search for optimal technological parameters of the non-consumable electrode welding.

The articles are arranged in five thematic topics, I) Strongly coupled (functional) hybrid methods (articles 1-3); II) Loosely coupled (functional) hybrid methods (articles 4-5); III) Transformational hybrid methods (articles 6-7); IV) Integrated methods (articles 8-12) and V) Distributed hybrid intelligent methods (article 13).