3,765 resources related to Backpropagation
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2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Cognitive Informatics (CI) is a cutting-edge and multidisciplinary research field that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software engineering, knowledge engineering, cognitive robots, scientific philosophy, cognitive linguistics, life sciences, and cognitive computing.
2013 15th International Conference on Transparent Optical Networks (ICTON)
ICTON addresses applications of transparent and all optical technologies in telecommunication networks, systems, and components. ICTON topics are well balanced between basic optics and network engineering. Interactions between those two groups of professionals are a valuable merit of conference. ICTON combines high level invited talks with carefully selected regular submissions.
2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
This is a general Electrical and Computer Engineering Conference which encompasses all aspects of these fields.
2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas)
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.
Garcia-Trevino, E.S.; Alarcon-Aquino, V. Electronics, Communications and Computers, 2006. CONIELECOMP 2006. 16th International Conference on, 2006
This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks.. This kind of network uses ...
Donat, Jean Saint; Bhat, Naveen; McAvoy, Thomas J. American Control Conference, 1990, 1990
Neural networks hold great promise for application in the general area of process control. This paper focuses on using a backpropagation network in an optimization based model predictive control scheme. Since analytical expressions for the gradient and Hessian of the neural net model can be derived and these expressions can be calculated in paralle, extremely fast computation times are possible. ...
Leonard, James A.; Kramer, Mark A. American Control Conference, 1990, 1990
Artificial neural networks trained by backpropagation have recently been applied to fault diagnosis problems. Backpropagation produces-decision surface that effectively separate training examples of different cases. However, separation of the training examples does not necessarily result in the most plausible or robust classifier. The decision surfaces may deviate from optimality and take on non-intuitive shapes because the decision surfaces are based ...
Sung Joo Park; Jin Seol Yang Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, 1994
The goal of this work is to develop a hierarchical neural network (HNN) architecture for providing intelligent control of complex urban traffic networks which are usually nonlinear and hard to model mathematically. Two types of neural networks, such as a global planning network and local control networks, are employed for traffic modeling and control. The experimental results indicate that the ...
Bertsekas, D.P. Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on, 1994
Proposes and analyzes nonlinear least squares methods, which process the data incrementally, one data block at a time. Such methods are well suited for large data sets and real time operation, and have received much attention in the context of neural network training problems. The author focuses on the extended Kalman filter, which may be viewed as an incremental version ...
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