The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Artificial Neural Networks
Abstract
Artificial neural networks are useful tools that have been applied successfully in a broad range of signal‐processing problems. From a signal‐processing perspective, their ability to adapt continuously to new data allows them to track changes in a signal over time, and their ability to learn from arbitrary, noisy data permits them to solve problems that cannot be handled adequately with some of the conventional statistical techniques. This chapter presents the fundamental principles of neural network modeling and illustrates some of the most commonly used neural network architectures for practical problem solving.
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Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740
indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,923
Self-Organization and Associative Memory
Kohonen, T. (1988) · Springer Series in Information Sciences · doi:10.1007/978-3-662-00784-6 · Cited by 3,812
Orthogonal least squares learning algorithm for radial basis function networks
Chen, S., Cowan, C. F. N., & Grant, P. M. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80341 · Cited by 3,362
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Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107077 · Cited by 1,442
Learning similarity with cosine similarity ensemble
Xia, P., Zhang, L., & Li, F. (2015) · Information Sciences · doi:10.1016/j.ins.2015.02.024 · Cited by 552
Arrhythmia Detection - A Machine Learning based Comparative Analysis with MIT-BIH ECG Data
Singh, V., Tewary, S., Sardana, V., & Sardana, H. K. (2019) · 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct45611.2019.9033665 · Cited by 24
Metadata
- Authors
- Abraham, A.
- Venue
- Handbook of Measuring System Design
- Publisher
- Wiley
- Published
- 2005-02-24
- OpenAlex
- W2752322585
- Cited by
- 339
- References
- 16
- Type
- other
- Language
- en
- Open access
- closed
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