Block-Recursive Least Squares Technique for Training Multilayer Perceptrons
Block-Recursive Least Squares Technique for Training Multilayer Perceptrons
The multilayer perception is one of the most commonly used types of feed-forward neural networks and it is used in a large number of applications. Its strength resides in its capacity of mapping arbitrarily complex non-linear functions by a convenient number of layers of sigmoidal non-linearities (Rumelhart et al., 1986). The Backpropagation algorithm is still the most used learning algorithm; it consists in the minimization of the Mean-Squared Error (MSE) at the network output performed by means of a gradient descent on the error surface in the space of weights.
- Roma Tre University Italy
- Sapienza University of Rome Italy
FEEDFORWARD NEURAL NETWORKS; RECURSIVE LEAST SQUARES TECHNIQUE; FAST LEARNING ALGORITHMS; PERTURBATION TECHNIQUES; BACKPROPAGATION ALGORITHMS, BACKPROPAGATION ALGORITHMS; FEEDFORWARD NEURAL NETWORKS; BLOCK RECURSIVE LEAST SQUARES ALGORITHM; PERTURBATION TECHNIQUES
FEEDFORWARD NEURAL NETWORKS; RECURSIVE LEAST SQUARES TECHNIQUE; FAST LEARNING ALGORITHMS; PERTURBATION TECHNIQUES; BACKPROPAGATION ALGORITHMS, BACKPROPAGATION ALGORITHMS; FEEDFORWARD NEURAL NETWORKS; BLOCK RECURSIVE LEAST SQUARES ALGORITHM; PERTURBATION TECHNIQUES
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