Paper
30 April 1992 Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks
Stelios C.A. Thomopoulos, Ioannis Papadakis, Haralambos Sahinoglou, Nickens N. Okello
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Abstract
Two different types of adaptive networks are considered for solving the centralized and distributed hypothesis testing problem. The performance of the two different types of networks is compared under different performance indices and training rules. It is shown that training rules based on the Neyman-Pearson criterion outperform error based training rules. Simulations are provided for data that are linearly and nonlinearly separable.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stelios C.A. Thomopoulos, Ioannis Papadakis, Haralambos Sahinoglou, and Nickens N. Okello "Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57911
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Sensors

Sensor fusion

Neurons

Data fusion

Neural networks

Binary data

Data modeling

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