Neural methodologies in rule-based expert systems.
Ernesto Burattini, Guglielmo Tamburrini (1996)
Mathware and Soft Computing
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Ernesto Burattini, Guglielmo Tamburrini (1996)
Mathware and Soft Computing
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Jadranka Jović (1997)
The Yugoslav Journal of Operations Research
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Izabela Rojek (2010)
Control and Cybernetics
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Nenad Dulanović, Dane Hinić, Dejan Simić (2008)
The Yugoslav Journal of Operations Research
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Andrzej Kasiński, Filip Ponulak (2006)
International Journal of Applied Mathematics and Computer Science
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In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods?...
Igor Vajda, Belomír Lonek, Viktor Nikolov, Arnošt Veselý (1998)
Kybernetika
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For general Bayes decision rules there are considered perceptron approximations based on sufficient statistics inputs. A particular attention is paid to Bayes discrimination and classification. In the case of exponentially distributed data with known model it is shown that a perceptron with one hidden layer is sufficient and the learning is restricted to synaptic weights of the output neuron. If only the dimension of the exponential model is known, then the number of hidden layers will...
Ernesto Burattini, A. Pasconcino, Guglielmo Tamburrini (1995)
Mathware and Soft Computing
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The problem of extracting more compact rules from a rule-based knowledge base is approached by means of a chunking mechanism implemented via a neural system. Taking advantage of the parallel processing potentialities of neural systems, the computational problem normally arising when introducing chuncking processes is overcome. Also the memory saturation effect is coped with using some sort of forgetting mechanism which allows the system to eliminate previously stored, but less often...
D. Zhang, Q. Jiang, X. Li (2005)
Mathware and Soft Computing
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This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting...
Eraslan, Ergün (2009)
Mathematical Problems in Engineering
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Jasna Soldić-Aleksić (2001)
The Yugoslav Journal of Operations Research
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Filip Ponulak (2008)
International Journal of Applied Mathematics and Computer Science
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In this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence...