37 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.
Mitrpanont, J.L.; Srisuphab, A. Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on, 2002
The paper presents the approach of the quantum complex-valued backpropagation neural network or QCBPN. The challenge of our research is the expected results from the development of the quantum neural network using complex-valued backpropagation learning algorithm to solve classification problems. The concept of QCBPN emerged from the quantum circuit neural network research and the complex-valued backpropagation algorithm. We found that ...
Xinxing Pan; Lee, B.; Chunrong Zhang Intelligent Energy Systems (IWIES), 2013 IEEE International Workshop on, 2013
Load forecasting plays a significant role in planning and operation of electrical power networks. Artificial neural networks have been extensively employed for load forecasting over the last 20 years, owing to their powerful non-linear mapping capability. A range of neural network training algorithms have been developed to solve different kinds of problems. Due to different goals of prediction and variation ...
Khan, A.U.; Bandopadhyaya, T.K.; Sharma, S. Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on, 2008
A challenging and daunting task is to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This paper presents a number of technical indicators, back propagation neural network and genetic based backpropagation neural network to predict the stock price of the day. Stock ...
Audhkhasi, K.; Osoba, O.; Kosko, B. Neural Networks (IJCNN), The 2013 International Joint Conference on, 2013
We prove that noise can speed convergence in the backpropagation algorithm. The proof consists of two separate results. The first result proves that the backpropagation algorithm is a special case of the generalized Expectation- Maximization (EM) algorithm for iterative maximum likelihood estimation. The second result uses the recent EM noise benefit to derive a sufficient condition for backpropagation training. The ...
Qing Song; Yeng Chai Soh; Lei Zhao Neural Networks, 2009. IJCNN 2009. International Joint Conference on, 2009
Elman networks (ENs) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer input (feedback from the hidden layer). Therefore, a standard on-line (real time) backpropagation (BP) algorithm, instead of the off-line backpropagation through time (BPTT) algorithm, can be applied for the training of ENs, which is usually called Elman backpropagation ...
Gori, Marco Introduction to Multilayer Perceptrons, 2009
The course introduces multilayer perceptrons in a self-contained way by providing motivations, architectural issues, and the main ideas behind the Backpropagation learning algorithm. In addition, the course shows how multilayer perceptrons can be successfully used in real-world applications
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