Since their introduction, Cellular Neural Networks 4] have turned out to be useful architectures for the solution of many problems, e. g. in image processing or in the simulation of partial diierential equations. Therefore, there have been several attempts to introduce cell circuits suitable for large-scale integration 3]. Up to now, all of these cells need energy and therefore power supply. Ju...

A Cellular Neural Network (CNN), also known as Cellular Nonlinear Network, is an array of dynamical systems (cells) or coupled networks with local connections only. Cells can be arranged in several configurations; however, the most popular is the two-dimensional CNNs organized in an eight-neighbor rectangular grid. Each cell has an input, a state, and an output, and it interacts directly only w...

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...

State Propagation Net is proposed bymotivations for simplifying an immune network model. State Propagation Net is a certain class of graph whose nodes have two states: active and inactive. The behavior of State Propagation Net is discussed by graph theoretical characterization. Although State Propagation Net is comparable with Life Game,Cellular Automata, and Majority Net, we discuss its relati...

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 ...