Recurrent Neural Network: TimeDelay Neural Network Implemented in Fast Compressed Neural Network For R

This a general overview of how a time delay network data flow would work.  Note that I've ommited the actual weight calculations/tuning etc. and I've generally skipped the hidden layer calculations and shown only a 2X time delay.  In theory you can show X time delays b

Red are the current set of inputs

Blue are the outputs from the previous evaluation

Green are the outputs from the run before that.  etc. etc.

Purple is the hidden layer

Teal is the output layer

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Data sources

Quick R Setup

Install Base R Packages

https://cran.r-project.org/bin/linux/ubuntu/

Download R Studio

https://www.rstudio.com/products/rstudio/download/

 

> library(reshape)
> library(RMySQL)
Loading required package: DBI

 

> library(fPortfolio)
Loading required package: timeDate
Loading required package: timeSeries
Loading required package: fBasics

 

Notes for Analysis of IBM

utilize R library pdfetch

library(pdfetch)

> pdfetch_YAHOO("IBM")
           IBM.open IBM.high IBM.low IBM.close IBM.volume IBM.adjclose
2007-01-03    97.18    98.40   96.26     97.27    9196800     82.10340
2007-01-04    97.25    98.79   96.88     98.31   10524500     82.98124
2007-01-05    97.60    97.95   96.91     97.42    7221300     82.23001

 

Utilizing ipred R libraries

>library(ipred)

ipredknn()  

Genetic Algorithm based feed forward neural network for probability and predictive analysis of large data sets

 

Genetic Algorithm based feed forward neural network for probability and predictive analysis of large data sets.

I’ve been working on AI related systems for 10 years with expert systems and genetic algorithms.  I’ve recently gotten more interested in their application to neural networks and statistical analysis capabilities.

Basic background you need to follow this:

https://en.wikipedia.org/wiki/Neuroevolution

Basic Feed forward neural network:

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