Optimization Based Model Using Fuzzy and Other Statistical Techniques Towards Environmental Sustainability, Karim Samsul Ariffin Abdul, Kadir Evizal Abdul, Nasution Arbi Haza
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.
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.
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.
Описание: For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.
Автор: Sorin Olaru; Alexandra Grancharova; Fernando Lobo Название: Developments in Model-Based Optimization and Control ISBN: 3319266853 ISBN-13(EAN): 9783319266855 Издательство: Springer Рейтинг: Цена: 16979.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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.
Introduction to Feature Selection.- Background.- Rough Set Theory.- Advance Concepts in RST.- Rough Set Based Feature Selection Techniques.- Unsupervised Feature Selection using RST.- Critical Analysis of Feature Selection Algorithms.- RST Source Code.
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.
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: Jos?-Luis Verdegay Название: Fuzzy Sets Based Heuristics for Optimization ISBN: 3642056113 ISBN-13(EAN): 9783642056116 Издательство: Springer Рейтинг: Цена: 29209.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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