back-propagation


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back-propagation

(Or "backpropagation") A learning algorithm for modifying a feed-forward neural network which minimises a continuous "error function" or "objective function." Back-propagation is a "gradient descent" method of training in that it uses gradient information to modify the network weights to decrease the value of the error function on subsequent tests of the inputs. Other gradient-based methods from numerical analysis can be used to train networks more efficiently.

Back-propagation makes use of a mathematical trick when the network is simulated on a digital computer, yielding in just two traversals of the network (once forward, and once back) both the difference between the desired and actual output, and the derivatives of this difference with respect to the connection weights.
References in periodicals archive ?
For the implementation of the system, a modification of the traditional Back-Propagation algorithm is developed.
Intimidating as these may sound, one does not need to dive into the depths of hardware acceleration, tensor algebra, or back-propagation in order to get a working model off the ground.
Implementation of back-propagation neural network for isolated Bangla speech recognition, International journal of information sciences and techniques, 3(4): 1-9, 2013.
The error back-propagation is a supervised learning algorithm that uses a gradient descent method to minimize the cost function, which is the mean square error between the ideal output and the actual output [27].
In this way, the sparse representation prior is effectively encoded in our deep network structure; all the coded components are trained jointly through back-propagation. This method includes three stages as shown in Figure 3.
In [47], the authors have proposed a combination of Improved SFLA (ISFLA) and Back-Propagation to train the neural network to diagnose early faults in rolling bearings.
Herrera, "Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers," Applied Soft Computing, vol.
In current assay, an artificial neural network modeling has been applied to determine the best coagulant rate in the water treatment plant according to a back-propagation training method.
Tsai, "Back-propagation network modeling for concrete pavement faulting using LTPP data," International Journal of Pavement Research and Technology, vol.
Xu, ""Soft decision" spectrum prediction based on back-propagation neural networks," in Proceedings of the IEEE International Conference on Computing, Management and Telecommunications (ComManTel '14), pp.
Miao, "MapReduce-based back-propagation neural network over large scale mobile data," in Proceedings of the 6th International Conference on Natural Computation (ICNC '10), pp.