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2. Radial Basis Function Neural Network.
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Artificial Neural Network

What is an Artificial Neural Network?

Artificial Neural Networks can be considered to be computer systems that use sets of algorithms which are inspired by and loosely modeled after biological neural networks that are found in animal brains.

They are designed to mimic the manner in which the human brain analyzes and processes information. They can be considered to be the very foundation of artificial intelligence and tend to have self-learning capabilities that help them become increasingly effective and accurate as they process more data.

They are modeled after the human brain and have thousands of artificial neurons that are interconnected by nodes. These nodes are processing units and are consist input and output units. 

Artificial neural networks refine their outputs by using backpropagation, also known as backward propagation of errors.

What are the advantages of artificial neural networks?

Here are the most significant advantages of artificial neural networks (ANNs):

1. Fault tolerance

Even if one or more cells of the ANN get corrupted, the generation of outputs will not be affected.


2. Parallel processing

Artificial neural networks have the ability to perform multiple functions simultaneously.


3. Training

They are able to bear long training times, can learn from events and make decisions based on similar events.


4. Gradual corruption

An artificial neural network does degrade and slow down over time. However, the ANN does not get corroded instantly.


5. Speed

ANNs are put to use in cases where a quick evaluation of the learned target function is necessary.

What are the disadvantages of artificial neural networks?

Artificial neural networks also come with their own disadvantages. Here are the most prominent ones:

1. Lack of transparency

The functioning of the ANNs is not clear, and the solutions reached do not come with explanations as to how or why they were reached.


2. Difficulties in introducing problems to artificial neural networks

Artificial neural networks work with numerical information. Therefore, before you introduce your problem to the artificial neural network, you need to convert it into numerical values.


3. No rules for structures

Artificial neural networks that no rules that govern their structures. You can reach an appropriate network structure through trial & error and experience.

What are the types of artificial neural networks?

There are multiple types of Artificial Neural Networks (ANNs) and they all have their own specific strengths. Here are some of the kinds of ANNs:

1. Feedforward Neural Network

These are the most basic ANNs. The data moves only in one direction, passing through various input nodes till it reaches the output node.

It calculates the sum of the products of all the inputs and their weights, this is then fed to the output.


2. Radial Basis Function Neural Network

They are mainly used in power restoration systems. They consider the distance of any point relative to the center. 

Their inner layers are where features are paired with the radial basis function and the output is considered when it is calculated in the succeeding time-step.


3. Multilayer Perceptron

They have three or more layers and are fully connected ANNs. They are used to classify data that can’t be linearly separated and they use a nonlinear activation function.

They are used in speech recognition and machine translation.


4. Convolutional Neural Network

They are used in image classification and signal processing. Convolutional neural networks contain one or more layers and can be pooled or entirely connected.


5. Recurrent Neural Network(RNN)

Recurrent neural networks remember all the information that has been calculated. In these networks, the output of the previous step is fed as input for the next step.


6. Sequence-To-Sequence Models

These models contain two RNNs. The input is processed by an encoder and the output is processed by a decoder. These models are mainly used in chatbots and machine translation systems.

7. Modular neural networks

Modular neural networks have performing subtasks independently without any interaction with each other, simply working independently to achieve the network’s goal.

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Artificial Neural Network

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is an Artificial Neural Network?

Artificial Neural Networks can be considered to be computer systems that use sets of algorithms which are inspired by and loosely modeled after biological neural networks that are found in animal brains.

They are designed to mimic the manner in which the human brain analyzes and processes information. They can be considered to be the very foundation of artificial intelligence and tend to have self-learning capabilities that help them become increasingly effective and accurate as they process more data.

They are modeled after the human brain and have thousands of artificial neurons that are interconnected by nodes. These nodes are processing units and are consist input and output units. 

Artificial neural networks refine their outputs by using backpropagation, also known as backward propagation of errors.

What are the advantages of artificial neural networks?

Here are the most significant advantages of artificial neural networks (ANNs):

1. Fault tolerance

Even if one or more cells of the ANN get corrupted, the generation of outputs will not be affected.


2. Parallel processing

Artificial neural networks have the ability to perform multiple functions simultaneously.


3. Training

They are able to bear long training times, can learn from events and make decisions based on similar events.


4. Gradual corruption

An artificial neural network does degrade and slow down over time. However, the ANN does not get corroded instantly.


5. Speed

ANNs are put to use in cases where a quick evaluation of the learned target function is necessary.

What are the disadvantages of artificial neural networks?

Artificial neural networks also come with their own disadvantages. Here are the most prominent ones:

1. Lack of transparency

The functioning of the ANNs is not clear, and the solutions reached do not come with explanations as to how or why they were reached.


2. Difficulties in introducing problems to artificial neural networks

Artificial neural networks work with numerical information. Therefore, before you introduce your problem to the artificial neural network, you need to convert it into numerical values.


3. No rules for structures

Artificial neural networks that no rules that govern their structures. You can reach an appropriate network structure through trial & error and experience.

What are the types of artificial neural networks?

There are multiple types of Artificial Neural Networks (ANNs) and they all have their own specific strengths. Here are some of the kinds of ANNs:

1. Feedforward Neural Network

These are the most basic ANNs. The data moves only in one direction, passing through various input nodes till it reaches the output node.

It calculates the sum of the products of all the inputs and their weights, this is then fed to the output.


2. Radial Basis Function Neural Network

They are mainly used in power restoration systems. They consider the distance of any point relative to the center. 

Their inner layers are where features are paired with the radial basis function and the output is considered when it is calculated in the succeeding time-step.


3. Multilayer Perceptron

They have three or more layers and are fully connected ANNs. They are used to classify data that can’t be linearly separated and they use a nonlinear activation function.

They are used in speech recognition and machine translation.


4. Convolutional Neural Network

They are used in image classification and signal processing. Convolutional neural networks contain one or more layers and can be pooled or entirely connected.


5. Recurrent Neural Network(RNN)

Recurrent neural networks remember all the information that has been calculated. In these networks, the output of the previous step is fed as input for the next step.


6. Sequence-To-Sequence Models

These models contain two RNNs. The input is processed by an encoder and the output is processed by a decoder. These models are mainly used in chatbots and machine translation systems.

7. Modular neural networks

Modular neural networks have performing subtasks independently without any interaction with each other, simply working independently to achieve the network’s goal.

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