The performance of Hopfield's neural net operating in synchronous and asynchronous modes is contrasted. Two interconnect matrices are considered: (1) the original Hopfield interconnect matrix; (2) the original Hopfield interconnect matrix with self-neural feedback. Specific attention is focused on techniques to maximize convergence rates and avoid steady-state oscillation. We identify two oscil...

This paper deals with outlier modeling within a very special framework: a segment-based speech recognizer. The recognizer is built on a neural net that, besides classifying speech segments, has to identify outliers as well. One possibility is to artificially generate outlier samples, but this is tedious, error-prone and significantly increases the training time. This study examines the alternat...

A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the ...

We describe a sequential neural network for harmonizing melodies in real time. It models aspects of human cognition. This neural network succeeds reasonably well, if we take into consideration the constraints imposed by real time processing. The model exploits eeciently the available sequential information. The net contains a sub-net for meter that produces a periodic index of meter, providing ...

In this work several approaches to prediction of natural gas consumption with neural and fuzzy neural systems are analyzed and tested. The data covers daily natural gas load in two different regions of Poland. Prediction strategies tested in the paper include: single neural net module approach, combination of three neural modules, temperature context based method, and application of fuzzy neura...

Knowledge transfer is widely held to be a primary mechanism that enables humans to quickly learn new complex concepts when given only small training sets. In this paper, we apply knowledge transfer to deep convolutional neural nets, which we argue are particularly well suited for knowledge transfer. Our initial results demonstrate that components of a trained deep convolutional neural net can c...

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net”), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qua...