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Posted by:
Deepak
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ARTIFICIAL NEURAL NETWORKS
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ARTIFICIAL NEURAL NETWORKS
History of Artificial Neural Networks
In the early 1940 s scientists came up with the hypothesis that neurons, fundamental, active cells in all animal nervous systems might be regarded as devices for manipulating binary numbers. Thus spawning the use of computers as the traditional replicants of Artificial Neural Networks.
To be understood is that advancement has been slow. Early on it took a lot of computer power and consequently a lot of money to generate a few hundred neurons. In relation to that consider that an ant s nervous system is composed of over 20,000 neurons and furthermore a human being s nervous system is said to consist of over 100 billion neurons! To say the least replication of the human s neural networks seemed daunting.
Today Artificial Neural Networks are being applied to an increasing number of real- world problems of considerable complexity.They are good pattern recognition engines and robust classifiers, with the ability to generalize in making decisions about imprecise input data. They offer ideal solutions to a variety of classification problems such as speech, character and signal recognition, as well as functional prediction and system modeling where the physical processes are not understood or are highly complex.
What is a Neural Network?
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:
1. A neural network acquires knowledge through learning.
2. A neural network s knowledge is stored within inter-neuron connection strengths known as synaptic weights.
The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.
The most common neural network model is the multilayer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown.
The MLP and many other neural networks learn using an algorithm called backpropagation. With backpropagation, the input data is repeatedly presented to the neural network. With each presentation the output of the neural network is compared to the desired output and an error is computed. This error is then fed back (backpropagated) to the neural network and used to adjust the weights such that the error decreases with each iteration and the neural model gets closer and closer to producing the desired output. This process is known as "training".
A good way to introduce the topic is to take a look at a typical application of neural networks. Many of today s document scanners for the PC come with software that performs a task known as optical character recognition (OCR). OCR software allows you to scan in a printed document and then convert the scanned image into to an electronic text format such as a Word document, enabling you to manipulate the text. In order to perform this conversion the software must analyze each group of pixels (0 s and 1 s) that form a letter and produce a value that corresponds to that letter. Some of the OCR software on the market use a neural network as the classification engine.
Of course character recognition is not the only problem that neural networks can solve. Neural networks have been successfully applied to broad spectrum of data-intensive applications, such as:
* Process Modeling and Control - Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant.
* Machine Diagnostics - Detect when a machine has failed so that the system can automatically shut down the machine when this occurs.
* Portfolio Management - Allocate the assets in a portfolio in a way that maximizes return and minimizes risk.
* Target Recognition - Military application which uses video and/or infrared image data to determine if an enemy target is present.
* Medical Diagnosis - Assisting doctors with their diagnosis by analyzing the reported symptoms and/or image data such as MRIs or X-rays.
* Credit Rating - Automatically assigning a company s or individuals credit rating based on their financial condition.
* Targeted Marketing - Finding the set of demographics which have the highest response rate for a particular marketing campaign.
* Voice Recognition - Transcribing spoken words into ASCII text.
* Financial Forecasting - Using the historical data of a security to predict the future movement of that security.
* Quality Control - Attaching a camera or sensor to the end of a production process to automatically inspect for defects.
* Intelligent Searching - An internet search engine that provides the most relevant content and banner ads based on the users past behavior.
* Fraud Detection - Detect fraudulent credit card transactions and automatically decline the charge.
The Neuron
Although it has been proposed that there are anything between 50 and 500 different types of neurons in our brain, they are mostly just specialized cells based upon the basic neuron. The basic neuron consists of synapses, the soma, the axon and dendrites. Synapses are connections between neurons - they are not physical connections, but miniscule gaps that allow electric signals to jump across from neuron to neuron. These electrical signals are then passed across to the soma which performs some operation and sends out its own electrical signal to the axon. The axon then distributes this signal to dendrites. Dendrites carry the signals out to the various synapses, and the cycle repeats.
Just as there is a basic biological neuron, there is basic artificial neuron. Each neuron has a certain number of inputs, each of which have a weight assigned to them. The weights simply are an indication of how important the incoming signal for that input is. The net value of the neuron is then calculated - the net is simply the weighted sum, the sum of all the inputs multiplied by their specific weight. Each neuron has its own unique threshold value, and it the net is greater than the threshold,the neuron fires (or outputs a 1), otherwise it stays quiet (outputs a 0). The output is then fed into all the neurons it is connected to.
Architecture
This area of neural networking is the "fuzziest" in terms of a definite set of rules to abide by. There are many types of networks - ranging from simple boolean networks (Perceptrons), to complex self-organizing networks (Kohonen networks),to networks modelling thermodynamic properties (Boltzmann machines)! There is, though, a standard network architecture.
The network consists of several "layers" of neurons, an input layer, hidden layers, and output layers. Input layers take the input and distribute it to the hidden layers (so-called hidden because the user cannot see the inputs or outputs for those layers). These hidden layers do all the necessary computation and output the results to the output layer, which (surprisingly) outputs the data to the user.
The Function of Artificial Neural Networks
Neural networks are designed to work with patterns - they can be classified as pattern classifiers or pattern associators. The networks can takes a vector (series of numbers), then classify the vector. For example, my ONR program takes an image of a number and outputs the number itself. Or my PDA32 program takes a coordinate and can classify it as either class A or class B (classes are determined by learning from examples provided). More practical uses can be seen in military radars where radar returns can be classified as enemy vehicles or trees.
Pattern associators takes one vector and output another. For example, my HIR program takes a dirty image and outputs the image that represents the one closest to the one it has learnt. Again, at a more practical level, associative networks can be used in more complex applications such as signature/face/fingerprint recognition.
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