Explicit representation of knowledge acquired from plant historical data using neural network

Baba, Kenji; Enbutu, Ichiro; Yoda, Mikio

doi:10.1109/ijcnn.1990.137838

Cited by 55 publications

(27 citation statements)

References 1 publication

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“…Different equations have been proposed based on the weights magnitude [1][2][3][4][5][6][7], all of them characterised by the calculation of the product of the weights w ij (connection weight between the input neuron i and the hidden neuron j) and v jk (connection weight between the hidden neuron j and the output neuron k) for each of the hidden neurons of the network, obtaining the sum of the calculated products. The equation below is that proposed by Garson [3] as representative of this type of analysis:…”

Section: Introductionmentioning

confidence: 99%

Numeric sensitivity analysis applied to feedforward neural networks

Moreno

Pol

2003

Neural Computing & Applications

110

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During the last 10 years different interpretative methods for analysing the effect or importance of input variables on the output of a feedforward neural network have been proposed. These methods can be grouped into two sets: analysis based on the magnitude of weights; and sensitivity analysis. However, as described throughout this study, these methods present a series of limitations. We have defined and validated a new method, called Numeric Sensitivity Analysis (NSA), that overcomes these limitations, proving to be the procedure that, in general terms, best describes the effect or importance of the input variables on the output, independently of the nature (quantitative or discrete) of the variables included. The interpretative methods used in this study are implemented in the software program Sensitivity Neural Network 1.0, created by our team.

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Section: Introductionmentioning

confidence: 99%

Numeric sensitivity analysis applied to feedforward neural networks

Moreno

Pol

2003

Neural Computing & Applications

110

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“…Causal index (CI) analysis [42] enables the identification of the relationships between specific inputs and outputs of an ANN model through quasi-qualitative analysis of the trained ANN connection weights. It can be used to identify the effect of the specific computer system measure on the identification of a known worm, thereby providing information about its effects on the host computer behavior.…”

Section: Causal Indices Analysismentioning

confidence: 99%

Using artificial neural networks to detect unknown computer worms

Stopel¹,

Moskovitch²,

Boger

et al. 2009

Neural Comput & Applic

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Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN's ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.

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“…A trained neural net may furnish a great deal of captured intelligence which is immediately accessible on-line for manufacturing control (Psaltis et al, 1987;Dagli and Lammers, 1989;Baba et al, 1990;Iwata et al, 1990;Madey and Weinroth, 1990;Rabelo et al, 1990;Weinroth and Madey, 1990). Such control can be automated entirely where necessary, or subject to manual override by the knowledgeable human operator, so that in the latter case the on-line artificial intelligence serves in the capacity of making complex advice immediately available.…”

Section: Introductionmentioning

confidence: 98%

Integration of neurocomputing and system simulation for modeling continuous improvement systems in manufacturing

1992

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The use of a neural network embedded in a larger general-purpose simulation system (GPSS) simulation used to model continuous improvement systems (CIS) policies in a factory setting is described. The neural network is used to accelerate the identification of an effective CIS policy by providing a more realistic simulation framework. The interface between general simulation theory and neural network simulation is examined. Neural networks, when embedded in larger general-purpose simulations, are found to offer the potential for improving on the capabilities of those simulations, in particular manufacturing simulations for continuous improvement of production processes.

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Explicit representation of knowledge acquired from plant historical data using neural network

Cited by 55 publications

References 1 publication

Numeric sensitivity analysis applied to feedforward neural networks

Numeric sensitivity analysis applied to feedforward neural networks

Using artificial neural networks to detect unknown computer worms

Integration of neurocomputing and system simulation for modeling continuous improvement systems in manufacturing

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