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Error Back Propagation Algorithm Characteristics

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Error Back Propagation Algorithm Pdf

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Back Propagation Algorithm Example

Back Propagation Learning Algor... byESCOM 7335views Back propagation network byHIRA Zaidi 1494views backpropagation in neural networks byAkash Goel 613views HOPFIELD NETWORK byankita pandey 17198views Backpropagation algo bynoT yeT woRkiNg !... 724views 2.5 backpropagation byKrish_ver2 776views Share SlideShare Facebook Twitter LinkedIn https://en.wikipedia.org/wiki/Backpropagation Google+ Email Email sent successfully! Embed Size (px) Start on Show related SlideShares at end WordPress Shortcode Link Back propagation 24,553 views Share Like Nagarajan Follow 0 0 4 Published on May 9, 2010 RevathyKarthika Nithya ... Published in: Education 5 Comments 71 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 http://www.slideshare.net/infobuzz/back-propagation 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 6 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. 6 months ago Reply Are you sure you want to Yes No Your message goes here SibesHusky There are various mistakes...especially in the algorithm of BPN..its good you didnt allow it for download...please cross check your algorithm again...assigning of i,j,k and h is not proper...if possible refer Sivanandam.. 3 years ago Reply Are you sure you want to Yes No Your message goes here Mahawi Mahmood at Master Student like 3 years ago Reply Are you sure you want to Yes No Your message goes here Dr. Shahzad Qureshi , Research at Private firm at Private firm 2 weeks ago Bhanvi chauhan 4 months ago vkm mittal 5 months ago Salman Khan 5 months ago meenakshisingla4 5 months ago Show More No Downloads Views Total views 24,553 On SlideShare 0 From Embeds 0 Number of Embeds 26 Actions Shares

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 http://elkniga.info/book_49_glava_61_4.16_VIRTUES_AND_LIMITATIONS_O.html 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 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 back propagation 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 back propagation algorithm 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 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 princi

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