An ANN model perceptron algorithm using generalized matrix inversion

Krishnamurthy, E. V. ; Krishnamurthy, Vikram (1994) An ANN model perceptron algorithm using generalized matrix inversion Parallel Computing, 20 (5). pp. 799-806. ISSN 0167-8191

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The application of generalized matrix inversion to artificial neural network model perceptron algorithm is described. This method permits real number inputs to the perceptron besides binary inputs, and can provide a solution depending upon error. Further it can provide recursive improvement to the solution depending upon whether the new input vector is linearly dependent or independent of the previous, input set of vectors; also this method permits to check for consistency of the solution. Thus redundancy and inconsistency can be checked. Also we point out the close relationship of Chu-Hsich algorithm to Ivakhnenko Group method of data handling (GMDH) that builds up a multinomial combination of input components. We illustrate this method using the problem of reconstruction of a magic square.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Artificial Neural Network (ann); Consistency Check Matrix; Perceptron; Generalized Inverses; Higher-order Correlation; Magic Square
ID Code:28211
Deposited On:15 Dec 2010 12:24
Last Modified:04 Jun 2011 06:57

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