By Kenji Suzuki (editor)

ISBN-10: 9535109359

ISBN-13: 9789535109358

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1997). A proposal of novel knowledge representation (Area representation) and the implementation by neural network. International Con‐ ference on Computational Intelligence and Neuroscience, III, 430-433. , & Osana, Y. (2008). Implementation of association of one-to-many as‐ sociations and the analog pattern in Kohonen feature map associative memory with area representation. Proceedings of IASTED Artificial Intelligence and Applications, Inns‐ bruck. , & Osana, Y. (2010). Kohonen feature map probabilistic associative memo‐ ry based on weights distribution.

In supervised learning, the input is associated with the output. If they are equal, learning is called auto-associative; if they are different, hetero-associative. 6. Back-propagation Back-propagation (BP) is a supervised algorithm for multilayer networks. It applies the generalized delta rule, requiring two passes of computation: (1) activation propagation (forward pass), and (2) error back propagation (backward pass). Back-propagation works in the following way: it propagates the activation from input to hidden layer, and from hidden to output layer; calculates the error for output units, then back propagates the error to hidden units and then to input units.

2. The perceptron Rosenblatt’s perceptron [47] takes a weighted sum of neuron inputs, and sends output 1 (spike) if this sum is greater than the activation threshold. It is a linear discriminator: given 2 points, a straight line is able to discriminate them. For some configurations of m points, a straight line is able to separate them in two classes (figures 3 and 4). Figure 3. Set of linearly separable points. Figure 4. Set of non-linearly separable points. The limitations of the perceptron is that it is an one-layer feed-forward network (non-recurrent); it is only capable of learning solution of linearly separable problems; and its learning algorithm (delta rule) does not work with networks of more than one layer.

### Artificial neural networks: Architectures and applications by Kenji Suzuki (editor)

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