Neural networks for decision support:

Schocken, Shimon; Ariav, Gad

doi:10.1016/0167-9236(94)90015-9

Cited by 52 publications

(14 citation statements)

References 18 publications

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“…Consequently, the pose of the coordinate system of the target with respect to the CMM can be obtained. Then the transformation matrix of the coordinate system of the moving platform with respect to the CMM is given TB= TpTrans-1 (12) where Trans is a translation transformation matrix of the coordinate system of the target with respect to the moving platform when the positioning of the parallel robot is zero point. Then, the transformation matrix of the coordinate system of the target with respect to the moving platform is given T = TB-1Tp (13) So, by the matrix T, any pose of the moving platform can be obtained.…”

Section: Pose Measurement and Coordinate Transformationmentioning

confidence: 99%

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Error Compensation for a Parallel Robot Using Back Propagation Neural Networks

Rong

Sun

et al. 2006

2006 IEEE International Conference on Robotics and Biomimetics

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This paper presents an error compensation method for a novel 6-DOF parallel robot using back propagation neural networks. The main error sources of the parallel robot are discussed. In order to improve the measuring accuracy, a specific target measured by a coordinate measuring machine is designed. The relationship of error map is established by the coordinate transformation. Experimental results showed that the pose accuracy of the parallel robot was improved by more than 60% after error compensation. A precise optical assembly was accomplished by the parallel robot after error compensation reliably and rapidly.Index Termserror compensation, parallel robot, back propagation neural networks, pose accuracy.

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Section: Pose Measurement and Coordinate Transformationmentioning

confidence: 99%

“…Back propagation neural networks are multilayer feedforward neural networks trained by back propagation of errors using gradient descent or conjugate gradient algorithms [11,12]. This type of neural network will consist generally of three layers: an input layer, an output layer, and one hidden layer, as shown in Fig.5.…”

Section: A Back Propagation Neural Networkmentioning

confidence: 99%

Error Compensation for a Parallel Robot Using Back Propagation Neural Networks

Rong

Sun

et al. 2006

2006 IEEE International Conference on Robotics and Biomimetics

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“…In this framework, many researchers and authors have strongly supported the view that ANNs can make a significant contribution to improving control and manufacturing systems (Kusiak 1990;Chryssolouris et al 1991;Burke and Ignizio 1992;Sabuncuoglu and Hommertzheim 1992;Udo and Gupta 1994;Posani and Dagli 1994;Schocken and Ariav 1994;Zhang and Huang 1995). A great challenge for today's companies is not only how to adapt to this changing business environment, but also how to draw a competitive advantage from the way in which they choose to do so.…”

Section: Introductionmentioning

confidence: 97%

Neural networks in production scheduling: Intelligent solutions and future promises

Metaxiotis

2003

Applied Artificial Intelligence

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The way their neurons are connected. * The specific kinds of computations their neurons do. * The way they transmit patterns of activity throughout the network. * The way they learn, including their learning rate.With regard to the arrangement of the neurons, we distinguish between layered and unlayered networks. In layered networks, the neurons are grouped in layers. Between the input and the output layer, which has external connections only, there are one or more hidden layers. The input data are transmitted from the input layer via the hidden layers to the output layer. The signal flow in layered networks is forward directed (feedforward 362K. Metaxiotis and J. Psarras Downloaded by [UQ Library]

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“…This review is motivated by a need for a broad understanding the various applications of ANN in health care and aids researchers interested in bridging the disciplines of organizational behaviour and computer science. Considering the sheer abundance in reported use and complexity of the area, it can be challenging to remain abreast of the new advancements and trends in applications of ANN [18]. Adopters of ANN or researchers new to the field of AI may find the scope and esoteric terminology of neural computing particularly challenging [18].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the sheer abundance in reported use and complexity of the area, it can be challenging to remain abreast of the new advancements and trends in applications of ANN [18]. Adopters of ANN or researchers new to the field of AI may find the scope and esoteric terminology of neural computing particularly challenging [18]. Literature suggests that current reviews on applications of ANN are limited in scope and generally focus on a specific disease [19] or a particular type of neural network [20], or they are too broad (i.e.…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial neural networks in health care organizational decision-making: A scoping review

2019

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Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

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Neural networks for decision support:

Cited by 52 publications

References 18 publications

Error Compensation for a Parallel Robot Using Back Propagation Neural Networks

Error Compensation for a Parallel Robot Using Back Propagation Neural Networks

Neural networks in production scheduling: Intelligent solutions and future promises

Applications of artificial neural networks in health care organizational decision-making: A scoping review

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