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Neural Computation, 1993
When training a feedforward neural network with backpropagation (Rumelhart et al. 1986), local minima are always a problem because of the nonlinearity of the system. There have been several ways to attack this problem: for example, to restart the training by selecting a new initial point, to perform the preprocessing of the input data or the neural ...
Liping Yang, Wanzhen Yu
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When training a feedforward neural network with backpropagation (Rumelhart et al. 1986), local minima are always a problem because of the nonlinearity of the system. There have been several ways to attack this problem: for example, to restart the training by selecting a new initial point, to perform the preprocessing of the input data or the neural ...
Liping Yang, Wanzhen Yu
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Adaptability of the backpropagation procedure
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003Possible paradigms for concept learning by feedforward neural networks include discrimination and recognition. An interesting aspect of this dichotomy is that the recognition-based implementation can learn certain domains much more efficiently than the discrimination-based one, despite the close structural relationship between the two systems.
Nathalie Japkowicz, Stephen Jose Hanson
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The complex backpropagation algorithm
IEEE Transactions on Signal Processing, 1991The backpropagation (BP) algorithm that provides a popular method for the design of a multilayer neural network to include complex coefficients and complex signals so that it can be applied to general radar signal processing and communications problems. It is shown that the network can classify complex signals. The generalization of the BP to deal with
Henry Leung, Simon Haykin 0001
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Kybernetes, 2001
The popular backpropagation algorithm for training neural nets is a special case of an earlier principle of significance feedback, which in turn has much in common with Selfridge’s “Pandemonium” and a connection with McCulloch’s “redundancy of potential command”.
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The popular backpropagation algorithm for training neural nets is a special case of an earlier principle of significance feedback, which in turn has much in common with Selfridge’s “Pandemonium” and a connection with McCulloch’s “redundancy of potential command”.
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Asymptotic Convergence of Backpropagation
Neural Computation, 1989We calculate analytically the rate of convergence at long times in the backpropagation learning algorithm for networks with and without hidden units. For networks without hidden units using the standard quadratic error function and a sigmoidal transfer function, we find that the error decreases as 1/t for large t, and the output states approach their ...
Gerald Tesauro, Yu He, Subutai Ahmad
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On the complex backpropagation algorithm
IEEE Transactions on Signal Processing, 1992A recursive algorithm for updating the coefficients of a neural network structure for complex signals is presented. Various complex activation functions are considered and a practical definition is proposed. The method, associated to a mean-square-error criterion, yields the complex form of the conventional backpropagation algorithm. >
BENVENUTO, NEVIO, F. Piazza
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Neocognitron learning by backpropagation
Systems and Computers in Japan, 1995AbstractIn the neural network for pattern recognition, when the selectivity of the feature‐extracting cell is lowered to enhance the generalizing power, a tendency is produced that patterns with similar shapes but belonging to different categories are confused.
Michihiro Ohno +2 more
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Backpropagation for Parametric STL
2019 IEEE Intelligent Vehicles Symposium (IV), 2019This paper proposes a method to evaluate Signal Temporal Logic (STL) robustness formulas using computation graphs. This method results in efficient computations and enables the use of backpropagation for optimizing over STL parameters. Inferring STL formulas from behavior traces can provide powerful insights into complex systems, such as longterm ...
Karen Leung +2 more
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Contrast enhancement for backpropagation
IEEE Transactions on Neural Networks, 1996This paper analyzes the effect of data-contrast to a backpropagation (BP) network and introduces a data preprocessing algorithm that can improve the efficiency of the standard BP learning. The basic idea is to transform input data to a range that associates the high-slope region of the sigmoid function where a relatively large modification of weights ...
Taek Mu Kwon, Hui Cheng
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1990 IJCNN International Joint Conference on Neural Networks, 1990
Backpropagation learning can execute at supercomputer speed from training data sets of unprecedented size when supercomputer main memory is backed with newly available parallel arrays of commodity disk drives. An efficient implementation of backpropagation learning was modified and extended to iterate through training data sets stored on a parallel ...
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Backpropagation learning can execute at supercomputer speed from training data sets of unprecedented size when supercomputer main memory is backed with newly available parallel arrays of commodity disk drives. An efficient implementation of backpropagation learning was modified and extended to iterate through training data sets stored on a parallel ...
openaire +1 more source