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27 December 1990 Theory and application of median-related filters
Neal C. Gallagher Jr., Edward J. Coyle
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Proceedings Volume 1347, Optical Information Processing Systems and Architectures II; (1990) https://doi.org/10.1117/12.23425
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States
Abstract
Many similarities exist between the class of nonlinear filters called stack filters and neural networks. In this chapter, we describe the relationships between these areas and how the theory of stack filters may be applied to the study of neural nets. The concept of a neural network represents one of the most original ideas ever to appear in the field of computing and artificial intelligence. However, many of the tools used to study these architectures are not innovative, but are the same tools used to analyze more conventional systems. Some of these tools are the mean squared error measure, orthogonality of functions, and optimization by minimizing energy, and correlation techniques. These are the same tools that have largely failed modern Al as well as the study of nonlinear systems. We believe these classical tools of linear theory will be of limited use in the study of neural systems.
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Neal C. Gallagher Jr. and Edward J. Coyle "Theory and application of median-related filters", Proc. SPIE 1347, Optical Information Processing Systems and Architectures II, (27 December 1990); https://doi.org/10.1117/12.23425
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KEYWORDS
Digital filtering
Nonlinear filtering
Neural networks
Aluminum
Artificial intelligence
Complex systems
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