Applications Of Artificial Neural Networks Iii

Artificial Neural Network Modelling

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Applications of Artificial Neural Networks III

Process Neural Networks

"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.