Neural Networks: A Systematic Introduction
by Raul Rojas
Publisher: Springer 1996
Number of pages: 509
Theoretical laws and models scattered in the literature are brought together in this book into a general theory of artificial neural nets. Starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. The book for readers who seek an overview of the field and wish to deepen their knowledge. Suitable as a basis for university courses in neurocomputing.
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by Milan Hajek - University of KwaZulu-Natal
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).
by Martin T. Hagan, et al.
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications.
by William Bialek - arXiv
We all are fascinated by the phenomena of intelligent behavior, as generated by our own brains. As physicists we want to understand if there are some general principles that govern the dynamics of the neural circuits that underlie these phenomena.
by Christian Dawson - MDPI AG
This Special Issue focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.