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Hybrid neural network and expert systems
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This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of integrating Neural and Symbolic Systems into Hybrid Systems.Abstract:
Preface. Part I: Fundamentals of Hybrid Systems. 1. Overview of Neural and Symbolic Systems. 2. Research in Hybrid Neural and Symbolic Systems. 3. Models for Integrating Systems. Part II: Case Studies of Hybrid Neural Network and Expert Systems. 4. LAM Hybrid System for Window Glazing Design. 5. Hybrid Systems Approach to Nuclear Plant Monitoring. 6. Chemical Tank Control System. 7. Image Interpretation via Fusion of Heterogeneous Sources using a Hybrid Expert-Neural Network System. 8. Hybrid Systems for Multiple Target Recognition. Part III: Analysis and Guidelines. 9. Guidelines for Developing Hybrid Systems. 10. Tools and Development Systems. 11. Summary and the Future of Hybrid Neural Network and Expert Systems. References. Index.read more
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Journal ArticleDOI
Application of a neural network technique to rainfall-runoff modelling
TL;DR: In this paper, a neural network technique was used for rainfall runoff modeling. But, the results suggest that the neural network shows considerable promise in the context of rainfall-runoff modelling but, like all such models, has variable results.
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A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems
TL;DR: Comparative research review of three famous artificial intelligent techniques in financial market shows that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns.
Journal ArticleDOI
Methods for combining the outputs of different rainfall–runoff models
TL;DR: In this paper, the authors promote the concept of combining the estimated output of different rainfall-runoff models to produce an overall combined estimated output to be used as an alternative to that obtained from a single individual rainfall runoff model.
BookDOI
Hybrid neural systems
Stefan Wermter,Ron Sun +1 more
TL;DR: An overview of Hybrid Neural Systems and Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks are presented.
References
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Journal ArticleDOI
Theory of Edge Detection
David Marr,Ellen C. Hildreth +1 more
TL;DR: The theory of edge detection explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround ∇2G filters acting on the image forms the basis for a physiological model of simple cells.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression
TL;DR: A three-layered neural network based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights finds coefficients for complete conjoint 2-D Gabor transforms without restrictive conditions for image analysis, segmentation, and compression.
Book
Neural Computing: Theory and Practice
TL;DR: The neural computing theory and practice book will be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.