Displaying similar documents to “The logic of neural networks.”

A new approach to image reconstruction from projections using a recurrent neural network

Robert Cierniak (2008)

International Journal of Applied Mathematics and Computer Science

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A new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in...

Reconstructing a neural net from its output.

Charles Fefferman (1994)

Revista Matemática Iberoamericana

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Neural nets were originally introduced as highly simplified systems of the neural system. Today they are widely used in technology and studied theoretically by scientists from several disciplines. (See e.g. [N]). However they remain little understood. (...)

Neural networks as a tool for georadar data processing

Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk (2015)

International Journal of Applied Mathematics and Computer Science

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In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from...

Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform

Krzysztof Halawa (2008)

International Journal of Applied Mathematics and Computer Science

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This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.