TY  - JOUR
A1  - Brause, Rüdiger W.
T1  - Sensor encoding using lateral inhibited, self-organized cellular neural networks
N2  - The paper focuses on the division of the sensor field into subsets of sensor events and proposes the linear transformation with the smallest achievable error for reproduction: the transform coding approach using the principal component analysis (PCA). For the implementation of the PCA, this paper introduces a new symmetrical, lateral inhibited neural network model, proposes an objective function for it and deduces the corresponding learning rules. The necessary conditions for the learning rate and the inhibition parameter for balancing the crosscorrelations vs. the autocorrelations are computed. The simulation reveals that an increasing inhibition can speed up the convergence process in the beginning slightly. In the remaining paper, the application of the network in picture encoding is discussed. Here, the use of non-completely connected networks for the self-organized formation of templates in cellular neural networks is shown. It turns out that the self-organizing Kohonen map is just the non-linear, first order approximation of a general self-organizing scheme. Hereby, the classical transform picture coding is changed to a parallel, local model of linear transformation by locally changing sets of self-organized eigenvector projections with overlapping input receptive fields. This approach favors an effective, cheap implementation of sensor encoding directly on the sensor chip. Keywords: Transform coding, Principal component analysis, Lateral inhibited network, Cellular neural network, Kohonen map, Self-organized eigenvector jets.
KW  - Transform coding
KW  - Principal component analysis
KW  - Lateral inhibited network
KW  - Cellular neural network
KW  - Kohonen map
KW  - Self-organized eigenvector jets
Y1  - 2010
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7954
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-79065
SN  - 0893-6080
SN  - 1879-2782
N1  - zuerst in: Neural networks, 9.1996, Nr. 1, S. 99-120
SP  - 1
EP  - 46
ER  -