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refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks ...
Handbook of Neural Network Signal Processing , Yu Hen Hu, Jenq-Neng Hwang, Sep 21, 2001, Technology & Engineering, 408 pages. The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time...
NeuralNetworksandLearningMachines(NeuralNetworksandLearningMachines( 上传者:weixin_37662589时间:2018-02-19 基于滑模观测器与PLL锁相环的PMSM无感FOC控制模型 鉴于《现代永磁同步电机控制原理》(袁雷编)一书中未涵盖锁相环无感模型的相关内容,特将相关资料整理分享,以供读者参考。
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning. The theoret
Computer vision, field of artificial intelligence in which programs attempt to identify objects represented in digitized images provided by cameras, thus enabling computers to “see.” Much work has been done on using deep learning and neural networks to
learning. Neural networks can also be described by the number of hidden nodes the model has, or in terms of how many input layers and output layers each node has. Variations on the classic neural network design enable various forms of forward and backward propagation of information among tiers...
在文中被引用了4次,提出局部响应归一化方法。 V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc. 27th International Conference on Machine Learning, 2010.提出ReLU激活函数。 Karen Simonyan et al. Very Deep Convolutional Network for Large-scale Image ...
E. (2010). Rectified linear units improve restricted Boltzmann machines. International Conference on Machine Learning, 807–814. [10] Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. International Conference on Artificial Intelligence and Statistics, 315–323...
Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning. Applications The framework can be used to learn semantic parsing and program synthesis from weak supervision (e.g., question-answer pairs), which is easier to collect and more...