On Fault Tolerant Training of Feedforward Neural Networks

Arad, Behnam S.; El-Amawy, A.

doi:10.1016/s0893-6080(96)00089-5

Cited by 33 publications

(14 citation statements)

References 4 publications

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“…The MLP architecture combines layers of perceptron-like processing elements (the neurons) connected by weighted connections (the synapses) to produce a network capable of dealing with complex nonlinearly separable mappings 29 . The distributed nature of the processing which takes place in a neural network contributes to the robustness of the system 30 , 31 …”

Section: Architecturementioning

confidence: 99%

Biologically inspired intelligent decision making

2013

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Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

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Section: Architecturementioning

confidence: 99%

Biologically inspired intelligent decision making

2013

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“…al, 1993), (Phatak, 1995), (Piuri et. al, 1991) and (Tchernev and Phatak, 2005) deal with models to evaluate and represent fault tolerance; (Arad and El-Amawy, 1997) and (Cavalieri and Mirabella, 1999) consider different solutions for improving fault tolerance and (Bolt, 1991), (Eickhoff et. al, 2005), (Elsimary et.…”

Section: Fault Tolerance In Artificial Neural Networkmentioning

confidence: 99%

“…Fault tolerance in Artificial Neural Networks (ANNs) has been a topic almost forgotten in the last decade. Only a few papers concerning this topic have been published after 1994: (Eickhoff and Rückert, 2005), (Protzel et al 1993), (Arad and El-Amawy, 1997), (Cavalieri and Mirabella, 1999), (Phatak, 1995). In spite of that, the intrinsic fault tolerance of Neural Networks is a very important characteristic.…”

Section: Introductionmentioning

confidence: 99%

A Matlab Tool for Analyzing and Improving Fault Tolerance of Artificial Neural Networks

Borralho

Fontes

Antunes

et al. 2009

IFAC Proceedings Volumes

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“…Only a few papers concerning this topic have been published after 1994: [1][2][3][4][5][6]. In spite of that, the intrinsic fault tolerance of neural networks is a very important characteristic.…”

Section: Introductionmentioning

confidence: 99%

FTSET-a software tool for fault tolerance evaluation and improvement

Morgado‐Dias

Borralho

Fontes

et al. 2010

Neural Comput & Applic

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It is commonly assumed that neural networks have a built-in fault tolerance property mainly due to their parallel structure. The international community of neural networks discussed these properties until 1994 and afterward the subject has been mostly ignored. Recently, the subject was again brought to discussion due to the possibility of using neural networks in areas where fault tolerance and graceful degradation properties would be an added value, like medical applications of nano-electronics or space missions. Nevertheless, the evaluation of fault tolerance and graceful degradation characteristics remained difficult because there were no systematic methods or tools that could be easily applied to a given Artificial Neural Networks application. The discussion of models is the first step for sorting ways of developing the fault tolerance capability and for building a tool that can evaluate and improve this characteristic. The present work proposes a fault tolerance model, presents solutions for improving it and introduces the Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks that evaluates and improves fault tolerance.

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On Fault Tolerant Training of Feedforward Neural Networks

Cited by 33 publications

References 4 publications

Biologically inspired intelligent decision making

Biologically inspired intelligent decision making

A Matlab Tool for Analyzing and Improving Fault Tolerance of Artificial Neural Networks

FTSET-a software tool for fault tolerance evaluation and improvement

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