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Artificial Neural Networks computational architecture

 

An artificial neural network (ANN) is a computational architecture for processing complex data using multiple interconnected processors and computational paths. 

Artificial neural networks are analogous to human brains, which means they can train and analyze large and complex data sets that are extremely difficult to process but can be done by using linear algorithms.

What’s Happening?  

A traditional digital computer can successfully solve many different tasks at a time. The instructions are processed very quickly, following the user’s expectations. However, it is powerless in situations where the user cannot comprehend the task themselves.  

And what’s worse is standard algorithms can’t work with “noisy” or incomplete data; even though real-life scenarios can be chaotic, the information is still processed. It’s precisely where an artificial neural network, a learning-capable computing system, can help.

Artificial neural networks are often called simulated neural networks (SNNs), but this expression refers to the brain of biological beings, the activity of which ANNs were initially designed to model.

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Structure of ANNs

The primary function of operating an artificial neural network is to form connections between many different processing elements, each of which serves as an analog of one neuron in the brain of a biological being. 

Neurons can be physically reproduced or simulated using a digital computer. First, each neuron receives a set of input signals. Then, taking into account the internal system of weight coefficients, each generates one output signal, which, as a rule, serves as input for another neuron.

Neurons are closely interconnected with each other and are organized into several different levels. The input layer receives the input data, and the output layer generates the final result. Typically, there are one or more hidden levels between these two levels. Therefore, it is impossible to predict or know exactly how data is transmitted in such a structure.

Training of ANNs

A system of randomly assigned weight coefficients is created first. It means that the networks “know nothing” and need to be trained to solve a particular problem. Then, two learning methods are applied depending on what problem the network is designed to solve.

A self-organizing artificial neural network (also known as the Kohonen network) is designed to process large amounts of data and must find patterns and determine the relationships between them. Scientists often use this kind of network to analyze experimental data.

A backpropagation network is trained by a human to perform specific tasks. During training, a person evaluates whether the result obtained by the artificial neural network is correct. If it is correct, those weight coefficients used to obtain it increase. 

If the result is incorrect, these weights are reduced. Networks of this type are often used to study the process of cognition and for applications that solve specific problems.

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How ANNs Can Improve Our Lives

Technological Impact

Artificial neural networks are helpful in various applications involving complex and often incomplete data. So, they are used for image and speech recognition. 

Artificial neural networks are also used in the latest versions of programs that perform the text-to-speech conversion. They’re based on many programs for analyzing handwritten texts (for example, those used in popular handheld computers).

Economical Impact 

Artificial neural networks in the economy can give the most incredible results.

Major financial institutions use them to increase productivity in issuer solvency assessment, discount calculation, targeted marketing, and credit appraisal. 

These systems typically provide only a few percent better accuracy than their predecessors, but they are very cost-effective given the amount of money involved. 

Artificial neural networks are now being used to analyze credit card transactions to identify transactions that are highly likely to be fraudulent.

Safety Impact

They are also used to detect other types of crimes. For example, they are used in detectors installed at many Western airports to analyze trace elements for the presence of explosive chemicals. 

At the Chicago Police Department, artificial neural networks are helping to detect corruption among police officers.

Final Thoughts 

We hope you enjoyed reading about the ANNs because the technology looks quite promising. Keep yourself updated with the latest internet packages to learn more about upcoming technology. Call on Cox Phone for more information regarding Cox internet plans and their availability. 

Artificial neural networks in the economy can give the most incredible results.

Major financial institutions use them to increase productivity in issuer solvency assessment, discount calculation, targeted marketing, and credit appraisal. 

These systems typically provide only a few percent better accuracy than their predecessors, but they are very cost-effective given the amount of money involved. 

Artificial neural networks are now being used to analyze credit card transactions to identify transactions that are highly likely to be fraudulent.

A system of randomly assigned weight coefficients is created first. It means that the networks “know nothing” and need to be trained to solve a particular problem. Then, two learning methods are applied depending on what problem the network is designed to solve.

 

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