Friday , 22 January 2021

Art. 02 – Vol. 21 – No. 3 – 2011

Signal Processing in Linear Neural Network

Nicoleta Liviana Tudor
Department of Information Technology. Mathematics, Physics, University Petrol & Gas of Ploieşti, Romania

Abstract: This paper addresses the problem of signal processing in neural network with linear units and includes an analysis of the representation of Boolean functions.

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is a single layer neural network. It was developed by Bernard Widrow and Ted Hoff at Stanford University in 1960. It is based on the McCulloch–Pitts neuron and consists of a weight, a bias and a summation function. The difference between Adaline and the standard perceptron (McCulloch-Pitts) is that in the learning phase the weights are adjusted according to the weighted sum of the inputs. There also exists an extension of linear neural network known as Madaline.

Learning is a process when free parameters (weights and bias levels) of a neural network are adapted and adjusted through a continuing process of stimulation by the environment in which the network is embedded.

Generic computing units are split into two functional parts: an integration function reduces the arguments to a single value and the output or activation function produces the output of this node taking that single value as its argument.

Keywords: signal processing, neural network, linear units, representation of Boolean functions.


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  1. ANDERSON, JAMES: Neural models with cognitive implications. In LaBerge & Samuels, Basic Processes in Reading Perception and Comprehension Models. Hillsdale. Erlbaum, 1977, pp. 27-90.
  2. DUMITRESCU, HARITON COSTIN: Reţele neuronale. Teorie şi aplicaţii. Editura Teora, Bucureşti, 1996, 460 p.
  3. KRÖSE, BEN; PATRICK VAN DER SMAGT: An introduction to Neural Networks. University of Amsterdam. Netherlands, 1996, 135 p.
  4. MOISE, ADRIAN: Reţele neuronale pentru recunoaşterea formelor. Editura MATRIX ROM, Bucureşti, 2005, 309 p.
  5. MATLAB Online Help, version 7.1. 2005
  6. ROJAS, RAÚL: Neural Networks A Systematic Introduction. Springer-Verlag, Berlin, 199
  7. SCHALKOFF, ROBERT: Pattern Recognition Statistical. Structural and Neural Approaches. John Wiley & Sons, New York, 1992.
  8. WIDROW; STERNS:Adaptive Signal Processing. New York, Prentice-Hall, 1985.
  9. WIDROW; WINTER: Neural Nets for Adaptive Filtering and Adaptive Pattern Recognition. Computer 21, 1988, pp. 25-39.
  10. YU HEN HU; JENQ-NENG HWANG: Handbook of Neural Network Signal Processing. Electrical Engineering & Applied Signal Processing Series. CRC Press. 2001, 408 p.

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