Neural
networks
 

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.

Neural Networks comprise a series of united elements or nodes, named artificial neurons that easily imitate themselves in an organic substance. Each contact can transfer one signal from one AN(artificial neuron) to another. 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 a set of non-continuous functions of some original material. The layers are the basement of the artificial neurons.

Goal of NN

The primary aim of the Neural Network is to settle the problems in the same approaches the personal brain does. Sensory systems can be applied for:

COMPUTER
VISION

SOCIAL
NETWORKS
CLEANING

SPEECH
IDENTIFICATION

GAMING

AUTOMATED
TRANSLATION

MEDICINE IDENTIFICATION
OF DISEASES

Allocation of the Neural Networks

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

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

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

The third NN is convolutional(it is also feedforward).

Tasks of the Neural Networks

CLASSIFICATION

CLUSTERING

FORECASTING

Neural networks development tools

The frameworks of the Neural Network are provided below:

CaffeCaffe
CUV LibraryCUV Library
Elastic thoughtElastic thought
MXNetMXNet
OpenDLOpenDL

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

CudamatCudamat
Eblearn.IshEblearn.Ish
HebelHebel
LibdeepLibdeep
MShadowMShadow
RNNLIBRNNLIB

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

PythonPython
C/C#C/C#
C++C++
RR
LushLush
JavaJava
PHPPHP
MatlabMatlab

The Neural Network development tools are suggested in the table:

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

LAW

COMMUNICATION

TEST FAILURES

POWER

DIAGNOSIS

The identification object is to define the accepted value of the characteristics.

Neural network’s latest development concentrates the issues the experts face during the effort. The next problem of the sensory web chain lies in the decision-making operations.

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