Process Neural Networks [recurso electrónico] : Theory and Applications / by Xingui He, Shaohua Xu.
Tipo de material: TextoSeries Advanced Topics in Science and Technology in ChinaEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: 240p. 78 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783540737629Tema(s): Computer science | Artificial intelligence | Optical pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Pattern RecognitionFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q334-342TJ210.2-211.495Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|---|---|
Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | Q334 -342 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 373197-2001 |
Navegando Biblioteca Electrónica Estantes, Código de colección: Colección de Libros Electrónicos Cerrar el navegador de estanterías (Oculta el navegador de estanterías)
Q334 -342 Foundations of Intelligent Systems | Q334 -342 Cognitive Reasoning | Q334 -342 Design of Modern Heuristics | Q334 -342 Process Neural Networks | Q334 -342 Resource-Adaptive Cognitive Processes | Q334 -342 Advances in Data Analysis, Data Handling and Business Intelligence | Q334 -342 Architecture-Based Design of Multi-Agent Systems |
Artificial Neural Networks -- Process Neurons -- Feedforward Process Neural Networks -- Learning Algorithms for Process Neural Networks -- Feedback Process Neural Networks -- Multi-aggregation Process Neural Networks -- Design and Construction of Process Neural Networks -- Application of 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.
19