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« Created by: Co0olCat on: 07/21/08 at 11:49:55 »

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White papers on Artificial Neural Networks (ANN) (Read 14227 times)
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White papers on Artificial Neural Networks (ANN)
07/21/08 at 11:35:29
 
This section lists white papers on Artificial Neural Networks (ANN).

Since we respect copyright, the section may only contain links to external sources and short abstracts.

For consistency, please use the following formatting convention:

Surname#1, N#1. and N#2., Surname#2 (or Surname#1, N#1 et al), date of publication, source

Paper Title
http://link

Abstract

Short abstract.
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Re: White papers on Artificial Neural Networks (ANN)
Reply #1 - 07/21/08 at 11:47:23
 
Forsgren, Anders and Kling, Robert, 2003

An implementation of recurrent neural networks for prediction and control of nonlinear dynamic systems
http://epubl.luth.se/1402-1617/2003/119/

Abstract

Neural networks have been used in identification and control of nonlinear dynamic systems for decades. Using recurrent neural networks which are themselves dynamic systems makes is possible to achieve models superior both to linear models and feedforward neural networks. Recurrent neural networks are able to capture the true hidden dynamics of nonlinear systems.

A software framework is developed which provides easy creation and training of arbitrary perceptron networks. An introduction to dynamic systems and system identification is given, as well as a short introduction to Kalman Filtering. The basics of neural networks in general and recurrent neural networks in particular are explained.

Recurrent multilayer perceptrons trained by the extended Kalman filter method are evaluated in a series of experiments. First, network structures are evaluated empirically by using them to identify and predict chaotic time series. Identification performance on both real world systems and simulated systems is compared to results obtained using linear models and feedforward neural networks. An identified model of a simulated nonlinear dynamic system demonstrates the use of a recurrent neural network in a predictive control scheme.
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Re: White papers on Artificial Neural Networks (ANN)
Reply #2 - 07/21/08 at 11:56:29
 
Wakeling, Joseph and Bak, Per, 30 October 2001

Intelligent systems in the context of surrounding environment
http://neuro.webdrake.net/papers/wakeling_bak_2001.pdf

Abstract

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the minority model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique ‘‘rogue’’ agents with higher memory values to take advantage of a majority population. We also show that agents’ analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in
the context of the surrounding environment (embodiment).
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Re: White papers on Artificial Neural Networks (ANN)
Reply #3 - 07/21/08 at 14:34:29
 
Kinzel, Wolfgang, 2000

Predicting and generating time series by neural networks: An investigation using statistical physics
http://citeseer.ist.psu.edu/378232.html

Abstract

An overview is given about the statistical physics of neural networks generating and analysing time series. Storage capacity, bit and sequence generation, prediction error, antipredictable sequences, interacting perceptrons and the application on the minority game are discussed. Finally, as a demonstration a perceptron predicts bit sequences produced by human beings.
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Re: White papers on Artificial Neural Networks (ANN)
Reply #4 - 07/21/08 at 14:44:20
 
Wakeling, Joseph Rushton, 2004

Adaptivity and `Per learning'
http://arxiv.org/abs/q-bio.NC/0403025

Abstract

One of the key points addressed by Per Bak in his models of brain function was that biological neural systems must be able not just to learn, but also to adapt--to quickly change their behaviour in response to a changing environment. I discuss this in the context of various simple learning rules and adaptive problems, centred around the Chialvo-Bak `minibrain' model [Neurosci. 90 (1999) 1137--1148].
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Re: White papers on Artificial Neural Networks (ANN)
Reply #5 - 03/15/09 at 16:43:53
 
Thank you so much for posting all of these great papers. I have just started my PhD in Algorithmic Trading, and have to say that my background is EEE Undecided.
I would like to make a great plan for myself, in order to know the starting point for my research, technique(NN's??), Maths,etc that I have to use on order to develop and a trading algo, from my set of high frequency data.
Appreciate any help and advice from you.
Mahnoosh
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Re: White papers on Artificial Neural Networks (ANN)
Reply #6 - 04/07/09 at 10:19:41
 
Hi,

I am developping algorithmic trading strategies for 5 years now...
I may advise you to take care of what kind of inputs you are using for your NN's.
Sometime the differences between batch process and o Smileyn-line process are really hudge.
Those papers are good if you are looking for prediction of price value on next bar step. You can also have a look on financial decision modelling tasks.
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