Aoming Liu. A short summary of this paper. Approach 2: Numerical gradient Intuition: gradient describes rate of change of a function with respect to a variable surrounding an infinitesimally small region Finite Differences: Challenge: how do we compute the gradient independent of each input?
Approach 3: Analytical gradient Recall: chain rule Assuming we know the structure of the computational graph beforehand… Intuition: upstream gradient values propagate backwards -- we can reuse them! What about autograd?
Identify intermediate functions forward prop 2. Compute local gradients 3. Xie, R. Casellas, C. Cole, and M. Li, eds. Sasai, M. Nakamura, T. Kobayashi, H. Kawakami, E. Yamazaki, and Y. The topics in this list come from the Optics and Photonics Topics applied to this article. Abstract We demonstrate the acquisition of signal power evolution and Raman gain spectra RGS in multi-span Raman-amplified links with digital backpropagation. View More Previous Article Next Article.
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Please download the article first. After downloading, please refresh this page. Contact your librarian or system administrator or Log in to access Optica Member Subscription or free downloads. They told us about the complexity of building this method. We thought that one control systems which have more complexity than a famous one, would be have a better performance.
We hoped this method could handle a blank spot of training of Neural Network which most of the people got the same problem with blank spot training. This wheel robot have 3 sonar sensors, and 2 motors. This robot realized in Webots simulator environment which completed all physics conditions so we can build many obstacles on it like a real world implemented. We train the robot without a cylindric shape obstacle.
For running process, we provided with 2 conditions, without an obstacle and with some obstacles. This conclude that CGBP still can not handle the blank spot of training, and it must to train with an obstacles to get the better successful.
Background Our laboratorium focused on robotics control development. Almost researcher have been know that Neural Network system must have an enough training to get a best performance.
For an example, we have wall follower robot. So, if we build a Neural Network system and really to be implemented on a real application like robot, we must consider about training variations. In our control research, we already developed T2-Fuzzy to solve a non linearity noise from sonar sensors, but we still looking for a best Neural Network method that have capability to handle a blank spot.
Consider this method have a unique complexity, we hope the result of our research about this method have a big successful to handle a blank spot of training. The main purpose of research is to get a winning parameters to make a best performance of Conjugate Gradient BackPropagation to handle a blank spot of training. To avoiding a hardware and mechanic problems, we use Webots as a robot simulator which have a physical plugins, and many things to be adding on simulation to get near to real world.
We build robot construction, make a robot field with many obstacles like a real world condition, and build a programming section on Webots. Because there are no problems with hardware and mechanic robot, we can easier to make conclusion that the Neural Network architecture percent influent the result of our research.
A complete Neural Network architecture consist as a feed forward architecture and back propagation architecture. Feed forward architecture will be active on learning process and running process, and back propagation only active on running process.
In feed forward architecture there is a section which researcher can use different ways to build their feed forward architecture. There are activation function variations, such as linear function, schwellwert function, sigmoid function, tangenshyperbolicus function, gauss function, and multiquadratische function [1][2]. And also there was research said that a non-polynomial activation can approximate any function [3]. We can see feed forward architecture on Figure 1. BackPropagation architecture also have a section that always developed by researcher with many purposes of that.
Figure 1. Feed forward architecture.
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