Process Neural Networks
Theory and Applications
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Beschreibung
"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.
Proposes concept and model of a process neural network for the first time Shows how a process neural network improves the expressing capability of artificial neural networks Proves theory and properties of process neural networks such as continuity, functional approximation ability, and computing power Shows how a process neural network can process time-varying signals directly and has extensive adaptability to solving many practical problems related to process Constructs multiform process neural network models and learning algorithms oriented to application
Autor*in
Xingui He
Themen in »Process Neural Networks«
ATSTC Artificial Neural Networks Pattern Recognition Process Control Signal Processing System Identification ZJUP algorithms artificial neural network cognition learning modeling neural network optimization system modeling