Already have an account? Get multiple benefits of using own account!
Login in your account..!
Remember me
Don't have an account? Create your account in less than a minutes,
Forgot password? how can I recover my password now!
Enter right registered email to receive password!
Examples of artificial neural networks:
Now here as an example consider a ANN that has been trained to learn the following rule categorising the brightness of 2x2 black and white pixel images also as: if it contains 3 or 4 black pixels then it is dark; but if it contains 2, 3 or 4 white pixels then it is bright. So here we can model this with a perceptron through sa pixel then they output +1 if the pixel is white and if -1 then the pixel is black. But here also if the input example is to be categorised as bright when the output unit produces a 1and if the example is dark when the output unit produces a 1. In fact if we choose the weights as in the following diagram where the perceptron will perfectly categorise any image of four pixels by dark or light according to our rule: as there are 4 input units and one for each pixel then they output +1 if the pixel is white and for -1 if the pixel is black. Here also the output unit produces a 1 if the input example is to be categorised as bright and if the example is dark then -1. Whether we choose the weights as in the following diagram then the perceptron will perfectly categorise any image of four pixels through dark or light according to our rule:
Furthermore details we see that in this case there the output unit has a step function through the threshold set to -0.1. But note there the weights in this network are all the same that is not true in the practical case. So now here it is convenient to make the weights going in to a node add up to 1 also, means it is possible to compare them easily. Thus the reason this network perfectly captures our notion of darkness and lightness is it means that if three white pixels are input so then three of the input units produce +1 and one input unit produces -1. Hence this goes into the weighted sum that giving a value of S = 0.25*1 + 0.25*1 + 0.25*1 + 0.25*(-1) = 0.5. As we seen this is greater than the threshold of -0.1, the output node produces +1 that relates to our notion of a bright image. Furthermore details we see that four white pixels will produce a weighted sum of 1 that is greater than the threshold so then two white pixels will produce a sum of 0 and also greater than the threshold. In fact if there are three black pixels then S will be -0.5 that is below the threshold thus the output node will output -1 so the image will be categorised as dark. Actually an image with four black pixels will be categorised as dark.
A full binary tree with 'n' non-leaf nodes have 2n+l nodes.
Explain the Architectural description languages Architectural description languages (ACLs) have been developed for the architectural description in analysis and design process
Problem Context and Specification : However the development of Inductive Logic Programming has been heavily formal in mathematical in nature it means the major people in the f
Modular programming denotes to the practice of writing a program as a sequence of independently assembled source files. Every source file is a modular program intended to be assemb
Identify three specific weaknesses in the design of the websites, derived from your analyses within Questions Part (c) and/or Question Part (a). There should be at least one weakne
Cache-Only Memory Access Model (COMA) As we have discussed previous, shared memory multiprocessor systems may use cache memories with each processor for deducting the execution
Q. Find the average of two values? Find the average of two values which are stored in ; Memory locations named FIRST and SECOND ; And puts result in memory location AVG
Erasable programmable read only memory (EPROM) This is a special type of PROM which can be erased by exposing it to ultraviolet (UV) light. Once it has been erased, it can be r
#questionabut diffraction ..
Explain LAN Topologies and its basic topologies. LAN Topologies: Network topology is a physical schematic that shows interconnection of the various users. There are four fund
Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!
whatsapp: +1-415-670-9521
Phone: +1-415-670-9521
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd