Title :
Dignet: an unsupervised-learning clustering algorithm for clustering and data fusion
Author :
Thomopoulos, Stelios C A ; Bougoulias, Dimitrios K. ; Wann, Chin-Der
Author_Institution :
Dept. of Electr. & Comput. Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
Dignet is a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior-to-noise interference, when the noise does not exceed a prespecified level of tolerance. The complexity of the proposed ANN, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of Dignet is based on the idea of competitive generation and elimination of attraction well in the pattern space. Dignet is used for detection and distributed decision fusion. Analytical and numerical results are included.<>
Keywords :
convergence of numerical methods; neural net architecture; pattern recognition; sensor fusion; unsupervised learning; Dignet; attraction well; behavior-to-noise interference; clustering; complexity; data fusion; dimensionality; distributed decision fusion; neuron requirements; self-organization; self-organizing artificial neural network; stored patterns; unsupervised-learning clustering algorithm; Artificial neural networks; Clustering algorithms; Control systems; Fusion power generation; Interference; Maximum likelihood detection; Neurons; Noise level; Research and development; Unsupervised learning;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
