3,757 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.
Mahoney, V.; Elhanany, I. Circuits and Systems, 2008. MWSCAS 2008. 51st Midwest Symposium on, 2008
Feedforward and recurrent neural networks have enjoyed great popularity in the field of machine learning. Hardware implementation of these networks, often using a backpropagation algorithm for learning, has many advantages over software implementation, the main advantage being speedup due to taking advantage of the inherent parallelism of neural networks. However, tradeoffs are often introduced with respect to speed, area, precision, ...
Kenue, S.K. Intelligent Vehicles '95 Symposium., Proceedings of the, 1995
Fuzzy control has recently emerged as a new technique of knowledge-based intelligent control in which precise knowledge of control algorithms is not required. The control knowledge is expressed in terms of membership functions for control parameters and a given rule set which defines the relationship among various parameters. Although this technique is robust, it cannot learn and adapt as parameters ...
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 ...
Parker, M.; Bryant, B.D. Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on, 2009
Backpropagation and neuroevolution are used in a Lamarckian evolution process to train a neural network visual controller for agents in the Quake II environment. In previous work, we hand-coded a non-visual controller for supervising in backpropagation, but hand-coding can only be done for problems with known solutions. In this research the problem for the agent is to attack a moving ...
Shiqian Wu; Meng Joo Er; Jun Liao American Control Conference, 1999. Proceedings of the 1999, 1999
A learning algorithm for dynamic fuzzy neural networks based on extended radial basis function (RBF) neural networks, which are functionally equivalent to TSK fuzzy systems, is proposed. The algorithm comprises four parts: (1) criteria of neurons generation; (2) allocation of parameters of RBF units; (3) weight adjustment; and (4) pruning technology. The algorithm has fast learning speed as the weights ...
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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