£78.96

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Springer Model Predictive Control Algorithms - Neural Network Approach

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Description

This comprehensive guide explores computationally efficient Model Predictive Control (MPC) techniques using neural network models, making advanced control systems accessible for practical implementation. The book covers suboptimal MPC algorithms that calculate linear approximations of models and predicted trajectories online, enabling real-time prediction without excessive computational overhead. Readers will find detailed implementation guidance for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models, and state-space neural models. The work also addresses MPC algorithms based on neural multi-models, providing engineers and researchers with actionable strategies for deploying efficient control systems. Part 3 of the Studies in Systems, Decision and Control series, this volume bridges the gap between theoretical control theory and practical neural network applications, offering solutions for systems where computational resources are constrained.

Key Features

Explores suboptimal MPC algorithms that successively calculate linear approximations of models and predicted trajectories during online execution.

Provides implementation details for feed forward perceptron neural models, neural Hammerstein models, and neural Wiener models in MPC systems.

Covers state-space neural models and MPC algorithms based on neural multi-models for advanced control applications.

Part of the Springer Studies in Systems, Decision and Control series (volume 3), offering academic credibility and comprehensive technical depth.

Focuses on computationally efficient techniques that reduce processing demands whilst maintaining control performance in real-time systems.

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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