3,761 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.
Wang Tao; Tian Yihui; Chen Yang Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on, 2009
In this paper, based on the traditional algorithm of TSK fuzzy reasoning model, a new fuzzy reasoning algorithm is proposed for two rules, two linguistic input variables and one output variable, in which the membership functions are Gaussian-type functions. By using neural network back- propagation algorithm, the parameters in the membership functions can be adjusted on-line without changing the rules. ...
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 ...
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 ...
Chow, T.W.S.; Gou Fei; Yam, Y.F. Control Applications, 1994., Proceedings of the Third IEEE Conference on, 1994
This paper describes the development of a self-feedback Sigma-Pi-linked (Σ-Π) backpropagation neural network and its applications to dynamical system identification. A self-feedback path is added to each neuron to generate the recursive effect. Each neuron output is recursively related by current input and its preceding output. The introduction of this self-feedback path enables the network to exhibit a dynamic characteristic. ...
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