Although there are many neural network FPGA architectures, there is no framework for designing large, high-performance neural networks suitable for the real world. In this paper, ...
We introduce a neural network, known as SONNETMAP, capable of automatic segmentation, learning and retrieval of melodies. SONNET-MAP is a synthesis of the SONNET (Self-Organizing ...
Abstract. Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM h...
Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the synaptic interactions are mediated via a nonlinear mechanism called shuntin...
Abstract. This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algor...
Abstract. One of the most influential factors in the quality of the solutions found by an evolutionary algorithm is the appropriateness of the fitness function. Specifically in ...
This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple pe...
We study the use of neural network algorithms in surface reconstruction from an unorganized point cloud, and meshing of an implicit surface. We found that for such applications, t...
Ioannis P. Ivrissimtzis, Won-Ki Jeong, Hans-Peter ...
This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals...
Witold Kinsner, V. Cheung, K. Cannons, J. Pear, T....
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods th...