Results 221 to 230 of about 15,646 (263)
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Neural Nets: An Evaluation and a Spreadsheet Implementation
Creativity and Innovation Management, 1996Attitudes to neural nets range from suspicion to uncritical admiration. This paper aims to introduce nets and to evaluate their strengths and weaknesses. The language is non‐technical, but the conceptual treatment is intended to be rigorous. A practical method for implementing a neural net on a spreadsheet is described, and sample results illustrated.
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2001
Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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Survival analysis and neural nets
Statistics in Medicine, 1994AbstractWe consider feed‐forward neural nets and their relation to regression models for survival data. We show how the back‐propagation algorithm may be used to obtain maximum likelihood estimates in certain standard regression models for survival data, as well as in various generalizations of these.
Liestøl, K. +2 more
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A neural net model for epilepsy
Journal of Theoretical Biology, 1977Abstract A neural net model based in our previous studies with randomly interconnected neural nets is presented here capable of exhibiting epileptic features. These features can be explained in terms of the structural and dynamical properties of the model. In addition, apart from the fact that this model can imitate epileptic phenomena, it might also
P A, Anninos, R, Cyrulnik
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On the quality of neural net classifiers
Artificial Intelligence in Medicine, 1994This paper describes several concepts and metrics that may be used to assess various aspects of the quality of neural net classifiers. Each concept describes a property that may be taken into account by both designers and users of neural net classifiers when assessing their utility.
Michael Egmont-Petersen +3 more
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Adaptive quadratic neural nets
Pattern Recognition Letters, 1991Abstract We present the theory and some results of a new algorithm for Artificial Neural Nets which behaves well on complex data sets. The algorithm uses adaptive quadratic forms as discriminant functions and is very fast compared with Back-Propagation— improvements of four orders of magnitude have been obtained.
Gek Sok Lim +2 more
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Applications of Hybrid Fuzzy Neural Nets and Fuzzy Neural Nets
1998The two topics of this chapter are: build hybrid fuzzy neural nets to equal fuzzy expert systems, fuzzy input-output controllers, and to evaluate certain fuzzy functions; and (2) show how first training a fuzzy neural net can solve the overfitting problem mentioned in Chapter 3.
James J. Buckley, Thomas Feuring
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Neural nets and the puzzle of intentionality
2002In this work, we ask epistemological questions involved in making the intentional behavior the object of physical and mathematical inquiry. We show that the subjective component of intentionality can never become object of scientific inquiry, as related to self-consciousness.
Gianfranco Basti, Antonio L. Perrone
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International Journal of Neural Systems, 1992
Neural network learning techniques for the recognition of decays of charged tracks are improved by adding the track momentum to the input. This allows the use of one single network for a wide range of energies. The efficiency of this method is compared with previous results and conventional methods and the behaviour of the nets is discussed in detail.
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Neural network learning techniques for the recognition of decays of charged tracks are improved by adding the track momentum to the input. This allows the use of one single network for a wide range of energies. The efficiency of this method is compared with previous results and conventional methods and the behaviour of the nets is discussed in detail.
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2007
Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. But it is lack of learning mechanism, which is the main weakness while modeling uncertain knowledge systems. Fuzzy neural Petri net (FNPN) is proposed in this paper, in which fuzzy neuron components are introduced into FPN as a sub-net model of FNPN ...
Hua Xu, Yuan Wang, Peifa Jia
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Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. But it is lack of learning mechanism, which is the main weakness while modeling uncertain knowledge systems. Fuzzy neural Petri net (FNPN) is proposed in this paper, in which fuzzy neuron components are introduced into FPN as a sub-net model of FNPN ...
Hua Xu, Yuan Wang, Peifa Jia
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