Business With Neural Networks

Harston, Craig T.

doi:10.1016/b978-0-12-546090-3.50028-0

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Also, specification of the material of the wall might then indicate if structural-acoustic coupling take place in each case. Figures 14 to 17 are for the room-pairs (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) and (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and show good agreement. Generally, the clamped condition gives the best results, again because the variations of SLD with frequency are not as great as for the simply supported case.…”

Section: Training With General Regression Neural Network Grnn Is a Nmentioning

confidence: 87%

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Prediction of Sound Insulation at Low Frequencies Using Artificial Neural Networks

Fora-Moncada

Gibbs

2002

Building Acoustics

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Low frequency noise, generated by outdoor and indoor sources, is an increasing problem and there is a need for methods of measurement and prediction of low frequency sound transmission, particularly between dwellings. This work presents a new approach to the low frequency sound transmission problem. Artificial Neural Networks, ANNs, are information processing systems that can store knowledge from their environment through a learning process and thus can be used in developing models for prediction. ANNs were applied to existing field data, in the standard frequency range, to confirm the classical mechanisms of sound transmission between dwellings. They then were applied to Finite Element Method modelled data for the frequency range 40-100 Hz. Results show the influence of parameters in sound transmission at low frequency that are not important at higher frequencies. Of particular significance is the main room dimension, normal to the plane of the party wall, and the edge conditions of the party wall. The results of applying ANN to sound transmission at low frequencies are promising but there remains a need for larger data sets, particularly to improve the prediction of equal room configurations where maximum acoustic coupling takes place.

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Section: Training With General Regression Neural Network Grnn Is a Nmentioning

confidence: 87%

“…Neural nets have been used to predict movement of stock currencies or improve marketing strategies [20] , also for the automatic recognition of hand-written characters or figures [21] . One of the first commercial applications of neural networks in signal processing was to suppress noise on a telephone line [22] .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Sound Insulation at Low Frequencies Using Artificial Neural Networks

Fora-Moncada

Gibbs

2002

Building Acoustics

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“…The availability of data in this field is rather restricted (Desay, Crook, & Overstreet, 1996). Hecht-Nielson Co. has developed a credit-scoring system that increased profitability by 27% by identifying good credit risk and poor credit risk (Harston, 1990). Glorfeld and Hardgrave (1996), Jagielska and Jaworski (1996), Leigh (1995), Piramuthu, Shaw, and Gentry (1994), Torsun (1996), among others, have also reported similar works on credit evaluation and scoring.…”

Section: Credit Scoringmentioning

confidence: 99%

Artificial Neural Networks

Sarker

2006

Artificial Neural Networks in Finance and Manufacturing

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The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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Anwendungsfelder Neuronaler Netze und ihre Umsetzung

Corsten¹,

May²

1996

Neuronale Netze in Der Betriebswirtschaft

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Business With Neural Networks

Cited by 7 publications

References 14 publications

Prediction of Sound Insulation at Low Frequencies Using Artificial Neural Networks

Prediction of Sound Insulation at Low Frequencies Using Artificial Neural Networks

Artificial Neural Networks

Anwendungsfelder Neuronaler Netze und ihre Umsetzung

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