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Effect of data standardization on neural network training
1996
Omega : The International Journal of Management Science
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A carefully designed experiment is used in which data from two-group classification problems were trained by feedforward networks. ...
Data transformation is a popular option in training neural networks. ...
The networks in this study were trained with the GRG2-based system of Subramanian and Hung [13] . GRG2 is a widely distributed nonlinear programming software [7] . ...
doi:10.1016/0305-0483(96)00010-2
fatcat:qjvang7cvbgjti5oah53hoqcvu
Forecasting with artificial neural networks:
1998
International Journal of Forecasting
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Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. ...
Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modeling issues, and (3) the future research directions. ...
Guidelines are either heuristic or Despite the many satisfactory characteristics of based on simulations derived from limited experi-ANNs, building a neural network forecaster for a ments. ...
doi:10.1016/s0169-2070(97)00044-7
fatcat:mh7hd4qvnjar7mkdvy63d2if5y
A simulation study of artificial neural networks for nonlinear time-series forecasting
2001
Computers & Operations Research
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ects of three main factors * input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. ...
Scope and purpose Interest in using arti"cial neural networks for forecasting has led to a tremendous surge in research activities in the past decade. ...
A GRG2-based system is used for training neural networks [49, 50] . ...
doi:10.1016/s0305-0548(99)00123-9
fatcat:w3qvflth4rbjjmu6wgf2spy4yy
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
1999
European Journal of Operational Research
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We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classi®cation theory. ...
In this paper, we present a general framework for understanding the role of arti®cial neural networks (ANNs) in bankruptcy prediction. ...
The bene®ts of GRG2 have been reported in the literature for many classi®cation problems [35, 42, 59] . This study uses a GRG2 based system to train neural networks. ...
doi:10.1016/s0377-2217(98)00051-4
fatcat:irbshy4t4be7dnia2xndlnxkde
Encountered Problems of Time Series with Neural Networks: Models and Architectures
[chapter]
2020
Recent Trends in Artificial Neural Networks - from Training to Prediction
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an adequate network model for forecasting, converts neural networks in an unstable technique, given that any change in training or in some parameter produces great changes in the prediction. ...
However, the high number of factors included in the configuration of the network, the training process, validation and forecasting, and the sample of data, which must be determined in order to achieve ...
Difficulties in the prediction of time series with neural networks The design of an artificial neural network is intended to ensure that for certain network inputs, it is capable of generating a desired ...
doi:10.5772/intechopen.88901
fatcat:7notvdrzsbar7f2whlsp4cbspa
Response Surface and Neural Network Techniques for Rocket Engine Injector Optimization
2001
Journal of Propulsion and Power
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This original data set is also employed to train a two-layered Radial Basis Neural Network (RBNN). ...
A data set of 45 design points from a semi-empirical model for a shear coaxial injector element using gaseous oxygen and gaseous hydrogen propellants is used to formulate response surfaces using quadratic ...
Radial Basis Neural Networks (RBNN) Radial Basis Neural Networks are trained by both Solverbe and Solverb for each injector design response, ERE and Q, using the original data set of 45 design points. ...
doi:10.2514/2.5755
fatcat:ig6ifjvj3rc2xejjwf4y5pmxsq
Genetic network inference: from co-expression clustering to reverse engineering
2000
Bioinformatics
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motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. ...
We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. ...
The authors would like to express their appreciation to Xiling Wen, Millicent Dugich and Stefanie Fuhrman for providing data on hippocampal development and injury, and to Stefanie Fuhrman for critically ...
doi:10.1093/bioinformatics/16.8.707
pmid:11099257
fatcat:lzocw6nm2be6tinz2zizkcspji
Optimisaton techniques: a futuristic approach for formulating and processing of pharmaceuticals
2016
Indian Journal of Pharmaceutical and Biological Research
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Designing and formulating an ideal pharmaceutical product is a very tedious job for a formulator as it comprises of multiple objectives. ...
too.The recent approach to optimise i.e. to achieve the best combination of product and process characteristics under the given conditions is by using Design of Experiment (DoE).Design of Experiment ( ...
ANN is a computer-based learning system that can be applied to quantify a nonlinear relationship between causal factors and pharmaceutical responses by means of iterative training of data obtained from ...
doi:10.30750/ijpbr.4.2.5
fatcat:l7kjavavkzbftou7kyuooi44m4
The Design and Validation of a Hybrid Information System for the Auditor's Going Concern Decision
1998
Journal of Management Information Systems
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
Synergy of artificial neural networks and knowledge-based expert systems for intelligent FMS scheduling. Proceedings of the Interna- tional Conference on Neural Networks, vol. 1. ...
Rabelo and Avula [39] use a hierarchical neural network system for intelligent control of a robotic arm. ...
doi:10.1080/07421222.1998.11518192
fatcat:ycxruaa4f5fqdarvxflbgnba2y
Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design
2018
Water
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form of traditional engineering experience/heuristics; and (iv) the lack of computational efficiency of network simulators required by modern population-based optimisation methods. ...
Two types of a design problem have been identified based on the field progression as follows: (i) a traditional design (i.e., theoretical or static design) of a WDS with a single construction phase for ...
A fuzzy set and fuzzy membership functions are defined for each performance criterion/each loading/each network element, based on previous experience [225] . ...
doi:10.3390/w10030307
fatcat:7g7cx57xpzhpzeg55lw4qeazly
Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process
2021
Engineering applications of artificial intelligence
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In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). ...
We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. ...
Acknowledgments The research presented in this paper has been conducted as part of the project EMPATHIC that has received funding from the European Union's Horizon 2020 research and innovation programme ...
doi:10.1016/j.engappai.2021.104189
fatcat:rmf7rv4infhtfasuvwcddjfk4m
Tolerance Design of Robot Parameters Using Generalized Reduced Gradient Algorithm
2017
International Journal of Materials Mechanics and Manufacturing
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In robot design, how to allocate tolerances for parts in manufacturing and assembling of robot is very important because this directly affects product quality and manufacturing cost. ...
Then, a mathematical model for tolerance allocation is formulated and transferred into the non-linear multi-variable optimization problem. ...
train the network that generates the part tolerances. ...
doi:10.18178/ijmmm.2017.5.2.298
fatcat:7vvsm32qnzfppeucdbgtfdesbm
A survey of industrial model predictive control technology
2003
Control Engineering Practice
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A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. ...
This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. ...
Acknowledgements The authors wish to thank the vendor representatives who responded to our MPC survey for their time and effort. ...
doi:10.1016/s0967-0661(02)00186-7
fatcat:zjvwtyuccfah3nojbbrx4ymhfm
Lost in optimisation of water distribution systems? A literature review of system operation
2017
Environmental Modelling & Software
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and others) linked with a network simulator EPANET (for example Alfonso et al. (2010), Dandy and Gibbs (2003)). ...
Optimisation of the operation of water distribution systems has been an active research field for almost half a century. ...
It describes the concept of a design of a real-time control system for WDSs. ...
doi:10.1016/j.envsoft.2017.02.009
fatcat:ryp5kbzafrb43lprrx5ptzzvme
Infinity Sensor: Temperature Sensing in GaN Power Devices using Peak di/dt
2018
2018 IEEE Energy Conversion Congress and Exposition (ECCE)
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Analysis of Energy Systems The analysis of energy systems is a prerequisite for identifying the design imperfections and promoting improvement strategies, which is mainly based on energy analysis and exergy ...
and promises to constitute a benchmark plant with a design efficiency of approximately 50%. ...
Baghsheikhi [82] used a soft computing system to realize the real-time exergoeconomic optimization of a steam power plant, which was developed based on experts' knowledge and experiences regarding the ...
doi:10.1109/ecce.2018.8558287
fatcat:6dtqnvu3tjef5nih4jqtztot34
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