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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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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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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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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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Backpropagation: past and future
IEEE International Conference on Neural Networks, 1988Some scientists have concluded that backpropagation is a specialized method for pattern classification, of little relevance to broader problems, to parallel computing, or to our understanding of the human brain. The author questions these beliefs and proposes development of a general theory of intelligence in which backpropagation and comparisons to ...
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On Combining Backpropagation with Boosting
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006Boosting is a method for learning combined classifiers. In a boosting ensemble of classifiers trained by the backpropagation algorithm, the learning rate takes much smaller value comparing with the backpropagation applied alone. We propose a method which overcomes the above drawback and test it on neuro-fuzzy systems constituting a classifier ensemble ...
Marcin Korytkowski +2 more
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Modelling with constructive backpropagation
Neural Networks, 1999Neural network methods have proven to be powerful tools in modelling of nonlinear processes. One crucial part of modelling is the training phase where the model parameters are adjusted so that the model performs the desired operation as well as possible. Besides parameter estimation, an important problem is to select a suitable model structure.
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