Haiquan Zhao – författare

This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also writtento accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing.

This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also writtento accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing.

This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also writtento accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing.

The two-volume set LNICST 641 and 642 constitutes the proceedings of the 5th International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2024, held in Hangzhou, China, during July 19–21, 2024.The 38 full papers and 17 short papers included in this book were carefully reviewed and selected from 126 submissions. They focus on rapid development of technology, the IoT, AI and mechanical automation. They were organized in topical sections as follows:Part I: Internet of Things Model Construction and Strategy Analysis; Intelligent Sensing and Fault Detection TechnologyPart II: Intelligent Sensing and Fault Detection Technology; Robot Modeling and Automation Control

The two-volume set LNICST 641 and 642 constitutes the proceedings of the 5th International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2024, held in Hangzhou, China, during July 19–21, 2024.The 38 full papers and 17 short papers included in this book were carefully reviewed and selected from 126 submissions. They focus on rapid development of technology, the IoT, AI and mechanical automation. They were organized in topical sections as follows:Part I: Internet of Things Model Construction and Strategy Analysis; Intelligent Sensing and Fault Detection TechnologyPart II: Intelligent Sensing and Fault Detection Technology; Robot Modeling and Automation Control

The two-volume set LNICST 641 and 642 constitutes the proceedings of the 5th International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2024, held in Hangzhou, China, during July 19–21, 2024.The 38 full papers and 17 short papers included in this book were carefully reviewed and selected from 126 submissions. They focus on rapid development of technology, the IoT, AI and mechanical automation. They were organized in topical sections as follows:Part I: Internet of Things Model Construction and Strategy Analysis; Intelligent Sensing and Fault Detection TechnologyPart II: Intelligent Sensing and Fault Detection Technology; Robot Modeling and Automation Control

The two-volume set LNICST 641 and 642 constitutes the proceedings of the 5th International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2024, held in Hangzhou, China, during July 19–21, 2024.The 38 full papers and 17 short papers included in this book were carefully reviewed and selected from 126 submissions. They focus on rapid development of technology, the IoT, AI and mechanical automation. They were organized in topical sections as follows:Part I: Internet of Things Model Construction and Strategy Analysis; Intelligent Sensing and Fault Detection TechnologyPart II: Intelligent Sensing and Fault Detection Technology; Robot Modeling and Automation Control

This book provides a comprehensive and in-depth exploration of adaptive filtering algorithms based on the Information Theoretic Learning (ITL). As a powerful alternative to traditional second-order statistical methods, ITL-based adaptive filtering algorithms are particularly effective in dealing with non-Gaussian noise. The book systematically introduces core ITL criteria such as minimum error entropy and maximum correntropy and extends these principles to the field of multidimensional signal processing and nonlinear adaptive filtering, demonstrating their effectiveness through modeling real-world signals like wind speed and temperature. In addition to single-node filtering, this book thoroughly investigates distributed adaptive filtering, addressing collaborative learning across networked systems. It further integrates graph signal processing, allowing for efficient modeling and analysis of signals defined on irregular or structured domains. Together, these contributions showcase ITL as a unified and powerful learning framework, advancing adaptive filtering theory and methodology across linear, nonlinear, distributed, and graph-based signal processing environments.