Optimization Based Model Using Fuzzy and Other Statistical Techniques Towards Environmental Sustainability, Karim Samsul Ariffin Abdul, Kadir Evizal Abdul, Nasution Arbi Haza

Автор: Patricia Melin; Oscar Castillo; Janusz Kacprzyk
Название: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization
ISBN: 331936961X ISBN-13(EAN): 9783319369617
Издательство: Springer
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Цена: 139310.00 T
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Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.

Автор: Kreinovich Vladik
Название: Statistical and Fuzzy Approaches to Data Processing, with Applications to Econometrics and Other Areas: In Honor of Hung T. Nguyen`s 75th Birthday
ISBN: 3030456188 ISBN-13(EAN): 9783030456184
Издательство: Springer
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Цена: 121110.00 T
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Описание: Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques.

Автор: Tshilidzi Marwala
Название: Finite Element Model Updating Using Computational Intelligence Techniques
ISBN: 1849963223 ISBN-13(EAN): 9781849963220
Издательство: Springer
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Цена: 139310.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book details advanced methods for tuning finite element simulations to measured data. Case studies demonstrate the principles and test the viability of the approaches. Coverage also critically analyzes the state of the art in FEM updating.

Автор: Seyedali Mirjalili; Jin Song Dong
Название: Multi-Objective Optimization using Artificial Intelligence Techniques
ISBN: 3030248348 ISBN-13(EAN): 9783030248345
Издательство: Springer
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Цена: 55890.00 T
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Описание: This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Автор: Manuel Barros; Jorge Guilherme; Nuno Horta
Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques
ISBN: 3642123457 ISBN-13(EAN): 9783642123450
Издательство: Springer
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Цена: 158380.00 T
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Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.

Автор: Manuel Barros; Jorge Guilherme; Nuno Horta
Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques
ISBN: 3642263232 ISBN-13(EAN): 9783642263231
Издательство: Springer
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Цена: 158380.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.

Автор: Sorin Olaru; Alexandra Grancharova; Fernando Lobo
Название: Developments in Model-Based Optimization and Control
ISBN: 3319266853 ISBN-13(EAN): 9783319266855
Издательство: Springer
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Цена: 113190.00 T
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Описание:

Introduction.- Part I. Complexity and Structural Properties of Linear Model Predictive Control.- 1. Complexity Certifications of First Order Inexact Lagrangian Methods for General Convex Programming: Application to Real-time MPC.- 2. Fully Inverse Parametric Linear/Quadratic Programming Problems via Convex Liftings.- 3. Implications of Inverse Parametric Optimization in Model Predictive Control.- Part II. Distributed-coordinated and Multi-objective Features of Model Predictive Control.- 4. Distributed Robust Model Predictive Control of Interconnected Polytopic Systems.- 5. Optimal Distributed-Coordinated Approach for Energy Management in Multisource Electric Power Generation Systems.- 6. Evolutionary-game-based Dynamical Tuning for Multi-objective Model Predictive Control.- Part III. Collaborative Model Predictive Control.- 7. A Model Predictive Control-based Architecture for Cooperative Path-following of Multiple Unmanned Aerial Vehicles.- 8. Predictive Control for Path Following. From Trajectory Generation to the Parameterization of the Discrete Tracking Sequences.- 9. Formation Reconfiguration using Model Predictive Control Techniques for Multi-Agent Dynamical Systems.- Part IV. Applications of Optimization-based Control and Identification.- 10. Optimal Operation of a Lumostatic Microalgae Cultivation Process.- 11. Bioprocesses Parameter Estimation by Heuristic Optimization Techniques.- 12. Real-time Experimental Implementation of Predictive Control Schemes in a Small-scale Pasteurization Plant.- Part V. Optimization-based Analysis and Design for Particular Classes of Dynamical Systems.- 13. An Optimization-based Framework for Impulsive Control Systems.- 14. Robustness Issues in Control of Bilinear Discrete-Time Systems - Applied to the Control of Power Converters.- 15. On the LPV Control Design and its Applications to Some Classes of Dynamical Systems.- 16. Ultimate Bounds and Robust Invariant Sets for Linear Systems with State-dependent Disturbances.- 17. RPI Approximations of the mRPI Set Characterizing Linear Dynamics with Zonotopic Disturbances.

Автор: Patricia Melin; Oscar Castillo; Janusz Kacprzyk
Название: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization
ISBN: 331917746X ISBN-13(EAN): 9783319177465
Издательство: Springer
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Цена: 156720.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems.

Автор: Jos?-Luis Verdegay
Название: Fuzzy Sets Based Heuristics for Optimization
ISBN: 3642056113 ISBN-13(EAN): 9783642056116
Издательство: Springer
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Цена: 194730.00 T
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Описание: The aim of this volume is to show how Fuzzy Sets and Systems can help to provide robust and adaptive heuristic optimization algorithms in a variety of situations. The book presents the state of the art and gives a broad overview on the real practical applications that Fuzzy Sets, based on heuristic algorithms, have.

Автор: Raza Muhammad Summair, Qamar Usman
Название: Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
ISBN: 9813291656 ISBN-13(EAN): 9789813291652
Издательство: Springer
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Цена: 83850.00 T
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Описание: This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book. This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.

Автор: Tshilidzi Marwala
Название: Finite Element Model Updating Using Computational Intelligence Techniques
ISBN: 1447157168 ISBN-13(EAN): 9781447157168
Издательство: Springer
Рейтинг:
Цена: 113180.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book details advanced methods for tuning finite element simulations to measured data. Case studies demonstrate the principles and test the viability of the approaches. Coverage also critically analyzes the state of the art in FEM updating.

Автор: R. Venkata Rao; Vimal J. Savsani
Название: Mechanical Design Optimization Using Advanced Optimization Techniques
ISBN: 1447159780 ISBN-13(EAN): 9781447159780
Издательство: Springer
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Цена: 130590.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a comprehensive review of the latest research and development trends for design optimization of mechanical elements and devices. The authors demonstrate optimization approaches using examples of various mechanical elements and devices.