NEURAL NETWORKS

NEURAL NETWORKS SERVICES

Neural networks are automated structures, which are caused by the example of natural sensory chains that form (set up) the animal head. That sort of the structures study to complete the assignments, by looking through the samples, without being planned out.

Neural Networks comprise the series of the united elements or nodes named artificial neurons that easily imitate themselves in the organic substance. Each contact can transfer one signal from one AN to different ones. The imitation neuron that gets the indicators may convert it and gives signs to neurons united with it.

The signal in the contact among neurons may be a real sum. The finished product is generated by the set of the non-continuous functions of some original material. The layers are the basement of the artificial neurons.

Goal of Neural Networks

The primary aim of the NN is to settle the problems in the same approaches the personal brain does.

Sensory systems can be applied for:

Computer vision Speech identification Automated translation Social networks cleaning Gaming Medicine identification of diseases

Allocation of the NN

There are many samples of the NNs, every of that requires the functional utilization and the levels of complications.

The most used kind of the NN is a feedforward NN (one way from the beginning till the end).

The second kind is a recurrent NN (many ways of transitions).

The third NN is convolutional.

Tasks of the Neural Networks

Categorization Clustering Prognosis

Neural networks development tools

CUV Library MXNet OpenDL Elastic thought

Neural networks development tools

The frameworks of the Neural Network are provided below:

Caffe CUV Library Elastic thought MXNet OpenDL

The set of the libraries for the neural networks advancement is the following:

Cudamat Eblearn.Ish Hebel Libdeep MShadow RNNLIB

The languages that are accepted for the Neural Network creation are listed:

Python C/C#C++R Lush Java PHP Matlab

The Neural Network development tools are suggested in the table:

ConvNet (Matlab toolbox)DeepLearnToolBox Deepnet (toolkit)Nengo (software)Pdnn (Python toolkit)

Development of neural networks for system identification

It refers to the original neural networks formation, the ability of neural networks and displays the reasons why neural networks are used in the system identification.

The suggested technical method defines that the SI is built by settling the attributes within a chosen standard, while the input conforms with the identified arrangement. Next is the prediction. This is the initial aim of the system identification.

The original goal is to establish the mathematical model of the physical process for the checked info.

Imitation is a substantially significant approach of researching, studying and realizing the world. In system imitation, 3 principles are applied such as division, choice, and advantage.

SI is a principal demand in the spheres:

CONTROL
COMMUNICATION
POWER
LAW
TEST FAILURES
DIAGNOSIS

FAQ

What is Neural networks?

➢Neural networks are automated structures, which are created by the example of natural sensory chains that form (set up) the animal brain. That sort of the structures learn to complete the assignments, by looking through the samples, without being directly programmed.

What are the main goals of NN?

➢The primary aim of the Neural Network is to settle the problems in the same approaches the personal brain does.

Where is NN used?

➢Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management..

Where can I look at your cases?

➢You can look at our cases here.

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