by Rolf Pfeifer, Dana Damian, Rudolf Fuchslin
Publisher: University of Zurich 2010
Number of pages: 109
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, non-supervised networks (competitive, Kohonen, Hebb), recurrent networks (Hopfield, CTRNNs - continuous-time recurrent neural networks), spiking neural networks, spike-time dependent plasticity, applications.
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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.
by Ben Krose, Patrick van der Smagt
This manuscript attempts to provide the reader with an insight in artificial neural networks. The choice of describing robotics and vision as neural network applications coincides with the neural network research interests of the authors.
Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.
by Ben Goertzel - Plenum Press
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.