£78.33

Springer Computationally Efficient MPC Algorithms - Neural Network

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Description

Explore advanced control strategies with this Springer publication on Computationally Efficient Model Predictive Control (MPC) Algorithms. This book provides a detailed look at suboptimal MPC techniques that utilize neural models to improve computational performance. Readers will find in-depth discussions on various suboptimal MPC algorithms, specifically those using linear approximations of models or predicted trajectories calculated on-line for prediction. The text covers essential implementation details for several neural structures, including feed forward perceptron neural models, neural Hammerstein models, neural Wiener models, and state-space neural models. Designed for those studying systems, decision, and control, this volume also examines MPC algorithms based on neural multi-models. It serves as a technical resource for understanding how neural networks can be applied to optimize prediction and control processes in complex systems.

Key Features

Covers suboptimal MPC algorithms using on-line linear approximations for model or predicted trajectory calculation.

Provides implementation details for feed forward perceptron neural models and state-space neural models.

Includes technical guidance on neural Hammerstein and neural Wiener model implementation.

Discusses MPC algorithms based on neural multi-models for predictive control applications.

Part of the Studies in Systems, Decision and Control series for specialized academic study.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
04 February 2014
Listed Since
28 November 2013

Barcode

No barcode data available

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