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!
Artificial neural network
The mathematical structure modeled on the human neural network and which is designed to attack number of statistical troubles, particularly in the areas of pattern recognition, learning multivariate analysis, and memory. The essential feature of such a structure is a network of the simple processing elements (arti?cial neurons) which are coupled together (either in the hardware or the software), so that they can cooperate with each other. From the set of 'inputs' and an associated set of parameters, the arti?cial neurons create an 'output' which provides a possible solution to the problem under analysis. In number of neural networks the relationship between the input received by the neuron and its output is determined by a general linear model. The most ordinary form is the feed-forward network which is basically an extension of idea of the perception. In this type of network the vertices can be numbered such that all the connections go from a vertex to one with the higher number; the vertices are set in layers, with connections only to the higher layers. This is explained in the figure drawn below. Each neuron sums its inputs to form a entire input and applies the function fj to xj to give the desired output yj. The links have weights wij which multiply signals travelling along with them by that factor. Number of ideas and activities familiar to statisticians can be expressed in a neural-network notation, consisting regression analysis, generalized additive models, and discriminate investigation. In any practical problem which occurs the statistical equivalent of specifying architecture of the suitable network is specifying a suitable model, and training the network to do well with reference to the training set is equivalent to estimating the parameters of the model provides a set of data.
Explanation of standard deviation and variance Describe the importance of standard deviation and variance, what they calculate and why they are required. Importance of char
Asymmetric proximity matrices Immediacy matrices in which the off-diagonal elements which are, in the i th row and j th column and the j th row and i th column, are not essent
Binomial Distribution Binomial distribution was discovered by swiss mathematician James Bernonulli, so this distribution is called as Bernoulli distribution also, this is a d
The Harmonic Mean is based on the reciprocals of numbers averaged. It is defined as the reciprocal of the arithmetic mean of the reciprocal of the given individual observations. Th
Simple Regression: The Teacher Preparation Research Team conducted a study of college students who took the Praxis II-a teacher certification examination. Some variables from
case study in heat power engineering
#question HOW TO TEST
This box plot displays the diversity wfood; the data ranges from 0.05710 being the minimum value and 0.78900 being the maximum value. The box plot is slightly positively skewed at
Importance and Application of probability: Importance of probability theory is in all those areas where event are not certain to take place as same as starting with games of
A. Do the correlation matrix table. B. Which variable (s) has the largest correlation coeffieient which is not a perfect correlation? C. Which variable (s) has the s
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: +91-977-207-8620
Phone: +91-977-207-8620
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd