Limitations Of Error Back Propagation Algo
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Статьи 4.16 VIRTUES AND LIMITATIONS OF BACK-PROPAGATION LEARNINGК оглавлению1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 back propagation algorithm in neural network 55 56 57 58 59 60 61 62 63 64 65 66 67
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68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 back propagation explained 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
Back Propagation Algorithm In Neural Network Ppt
122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 forward propagation 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 The back-propagation algorithm has emerged as the most popular algorithm for the supervised training of multilayer perceptrons. Basically, it is a gradient (derivative) technique and not an optimization technique. Back-propagation has two distinct properties: • It is simple to compute locally. • It performs stochastic gradient descent in weight space (for pattern-by-pattem updating of synaptic weights). These two properties of back-propagation learning in the context of a multilayer perceptron are responsible for its advantages and disadvantages. Connectlonism The back-propagation algorithm is an exarople of a connectionist paradigm that relies on local computations to discover the information-processing capabilities of neural networks. This form of computational restriction is referred to as the locality constraint, in the sense that the computation performed by the neuron is influenced solely by those neurons that are in physical contact with it. The use of local computations in the design of artificial neural networks is usually advocated for three principal reasons: 1. Artificial neur
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primary and merge this question into it? MERGE CANCEL exists and is an alternate of . Merge this question into Split learning representations by back-propagating errors and merge into it SAVE CANCEL Edit Answered by The WikiAnswers Community Making the world better, one answer at a time. The gradient descent algorithm is generally very slow because it requires small learning http://elkniga.info/book_49_glava_61_4.16_VIRTUES_AND_LIMITATIONS_O.html rates for stable learning. The momentum variation is usually faster than simple gradient descent, because it allows higher learning rates while maintaining stability, but it is still too slow for many practical applications. These two methods are normally used only when incremental training is desired. You would normally use Levenberg-Marquardt training for small and medium size networks, if you have enough memory available. If memory is a problem, then there http://www.answers.com/Q/The_limitation_of_Error_Backpropagation_Algorithm_and_its_Application are a variety of other fast algorithms available. For large networks you will probably want to use trainscg or trainrp. Multilayered networks are capable of performing just about any linear or nonlinear computation, and can approximate any reasonable function arbitrarily well. Such networks overcome the problems associated with the perceptron and linear networks. However, while the network being trained might theoretically be capable of performing correctly, backpropagation and its variations might not always find a solution The gradient descent algorithm is generally very slow because it requires small learning rates for stable learning. The momentum variation is usually faster than simple gradient descent, because it allows higher learning rates while maintaining stability, but it is still too slow for many practical applications. These two methods are normally used only when incremental training is desired. You would normally use Levenberg-Marquardt training for small and medium size networks, if you have enough memory available. If memory is a problem, then there are a variety of other fast algorithms available. For large networks you will probably want to use trainscg or trainrp. Multilayered networks are capable of performing just about any linear or nonlinear computation, and can approximate any reasonable function arbitrarily well. Such networks overcome the problems
1986)] is the slow pace at which backprop learns from examples. Even on simple benchmark problems, a back-propagation network may require many thousands of epochs to learn the desired behavior from examples. (An epoch is defined as one pass through the entire set of training examples.) We have http://changelog.ca/quote/2011/11/21/why_is_back-propagation_learning_so_slow attempted to analyze the reasons why backprop learning is so slow, and we have identified two http://www.slideshare.net/infobuzz/back-propagation major problems that contribute to the slowness. We call these the step-size problem and the moving target problem. There may, of course, be other contributing factors that we have not yet identified. [...] The Step-Size Problem The step-size problem occurs because the standard back-propagation method computed only ∂E⁄∂w, the partial first derivative of the overall error function with respect to each weight in back propagation the network. Give these derivatives, we can perform a gradient descent in weight space, reducing the error with each step. It is straightforward to show that if we take infinitesimal steps down the gradient vector, running a new training epoch to recompute the gradient after each step, we will eventually reach a local minimum of the error function. Experience has shown that in most situations this local minimum will be a global minimum as well, or at least "good enough" back propagation algo solution to the problem at hand. In a practical learning system, however, we do not want to take infinitesimal steps; for fast learning, we want to take the largest steps that we can. Unfortunately, if we choose a step size that is too large, the network will not reliably converge to a good solution. In order to choose a reasonable step size, we need to know not just the slope of the error function, but something about its higher-order derivatives -- its curvature -- in the vicinity of the current point in weight space. This information is not available in the standard back-propagation algorithm. [...] [...] The Moving Target Problem A second source of inefficiency in back-propagation learning is what we call the moving target problem. Briefly stated, the problem is that each unit in the interior of the network is trying to evolve into a feature detector that will play some useful role in the network's overall computation, but its task is greatly complicated by the fact that all the other units are changing at the same time. The hidden units in a given layer of the [artificial neural] net[work] cannot communicate with one another directly; each unit sees only its inputs and the error signal propagated back to it from the network's outputs. The error signal defines the problem that the unit is trying to solve, but this problem changes constantly. Instead of a situation i
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details. SlideShare Explore Search You Upload Login Signup Home Technology Education More Topics For Uploaders Get Started Tips & Tricks Tools Back propagation Upcoming SlideShare Loading in …5 × 1 1 of 44 Like this presentation? Why not share! Share Email The Back Propagation Learning Algor... byESCOM 7358views Back propagation network byHIRA Zaidi 1503views backpropagation in neural networks byAkash Goel 627views HOPFIELD NETWORK byankita pandey 17275views Backpropagation algo bynoT yeT woRkiNg !... 729views 2.5 backpropagation byKrish_ver2 789views Share SlideShare Facebook Twitter LinkedIn Google+ Email Email sent successfully! Embed Size (px) Start on Show related SlideShares at end WordPress Shortcode Link Back propagation 24,714 views Share Like Nagarajan Follow 0 0 4 Published on May 9, 2010 RevathyKarthika Nithya ... Published in: Education 5 Comments 72 Likes Statistics Notes Full Name Comment goes here. 12 hours ago Delete Reply Spam Block Are you sure you want to Yes No Your message goes here Post Vishanti Burden My colleagues were looking for TSP-1-C a few weeks ago and were informed of a great service that hosts lots of sample forms . If others require TSP-1-C too , here's a link http://goo.gl/au5UUe 4 months ago Reply Are you sure you want to Yes No Your message goes here Karan Nainwal , Student at APS Pune MAIL AT US : kamalmehra79531@gmail.com 7 months ago Reply Are you sure you want to Yes No Your message goes here Karan Nainwal , Student at APS Pune CAN YOU PLEASE MAIL US THIS PPT, ITS VERY VERY IMPORTANT, THANK YOU. 7 months ago Reply Are