In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are dis...

Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomenaEstablishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performanceIncludes case studies of several nonlinear stochastic systemsInvestigates the phenomena of incomplete information, including missing/degraded measurements, ...

In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly oc...

This open access book presents the proceedings of the 10th International Conference on Advances in Construction Machinery and Vehicle Engineering (ICACMVE 2025), highlighting the latest advancements and innovative practices in the field. Focusing on the challenges in mechanical design, control systems, and smart manufacturing, the selected papers cover a range of topics including innovative design strategies, intelligent systems and autonomous technologies, electrification and sustainable ...

The field's leading text, now completely updated.Modeling dynamical systems — theory, methodology, and applications.Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-f...

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, a...

The latest edition of this classic is updated with new problem sets and materialThe Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distor...

The authoritative guide to modeling and solving complex problems with linear programming—extensively revised, expanded, and updatedThe only book to treat both linear programming techniques and network flows under one cover, Linear Programming and Network Flows, Fourth Edition has been completely updated with the latest developments on the topic. This new edition continues to successfully emphasize modeling concepts, the design and analysis of algorithms, a...

Practical Approaches to Reliability Theory in Cutting-Edge ApplicationsProbabilistic Reliability Models helps readers understand and properly use statistical methodsand optimal resource allocation to solve engineering problems.The author supplies engineers with a deeper understanding of mathematical models while alsoequipping mathematically oriented readers with a fundamental knowledge of the engineeringrelatedapplications a...

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applicationsMore and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides t...

The purpose of this book is to present the main methods of static and dynamic optimization. It has been written within the framework of the European Union project – ERRIC (Empowering Romanian Research on Intelligent Information Technologies), funded by the EU’s FP7 Research Potential program and developed in cooperation between French and Romanian teaching researchers.Through the principles of various proposed algorithms (with additional references) this book allows the interested ...

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in...