Ebook: Process Neural Networks: Theory and Applications

"Process Neural Network: Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks and enhances the expression capability for practical problems, with broad applicability to solving problems relating to processes in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are closely examined. The application methods, network construction principles, and optimization algorithms of process neural networks in practical fields, such as nonlinear time-varying system modeling, process signal pattern recognition, dynamic system identification, and process forecast, are discussed in detail. The information processing flow and the mapping relationship between inputs and outputs of process neural networks are richly illustrated.

Xingui He is a member of Chinese Academy of Engineering and also a professor at the School of Electronic Engineering and Computer Science, Peking University, China, where Shaohua Xu also serves as a professor.

Part of the innovative series Advanced Topics in Science and Technology in China, this book sets forth the concept and model for a process neural network for the first time. You’ll learn how a process neural network expands the mapping relationship between the input and output of traditional neural networks. You’ll also discover how these networks greatly enhance the expression capability of artificial neural networks.

With its problem-solving approach, the book demonstrates how and why a process neural network can process time-varying signals directly and therefore has great potential for solving many practical process issues. The authors provide strict proof for theoretical problems such as continuity, functional approximation capability, and computing capability. Application methods, network construction principles, and optimization algorithms of process neural networks in practical fields are covered in detail.

Throughout this volume, detailed illustrations help you visualize the information processing flow and the mapping relationship between inputs and outputs of process neural networks.