If you are into the latest technology, you probably have heard about artificial neural networks. Yes, the word neural hails from the human nervous system called a neuron. The technology mimics the neuron system in the human brain; hence it is called an artificial neural network or also known as ANN. ANN has become very famous since its application give a huge impact on modern technology.
Though given the name as a neural network, but the system does not much illustrate the real human neural system. An artificial neural network is a series of algorithms that resembled the human brain to recognize a pattern. The system can interpret data through labeling, machine perception, or cluster input. In general, the system helps classify and cluster unlabeled data based on the example inputs. If you are interested in the ANN, here is anything you need to know about the technology.
- How does an Artificial Neural Network work?
- Where artificial network are used?
- What are the types of an artificial neural networks?
- What are the main components of artificial neural networks?
- What is a multilayer neural network?
- What is a backpropagation algorithm in a neural network?
- Download Artificial Neural Network PowerPoint Presentation:
- What are neural networks best for?
- Examples of Artificial Neural Network
- What are the advantages of artificial neural networks?
- What are the disadvantages of an artificial neural network?
- Few websites to refer:
How does an Artificial Neural Network work?
An artificial neural network is originally designed to self-program and adapts using algorithms to categorize and understand images, concepts, and photographs. The neural system receives inputs from other sources in form of images and patterns. To be able to recognize the input, developers design the artificial network into layers thus it operates in parallel.
The input layer resembles the dendrites on the human brain while the hidden layer resembles the body cells and is placed in between input and output layers and the output layer resembles the synaptic output of the brain. The artificial neurons take the inputs from hidden layers according to synaptic weight. Synaptic weight is the strength or amplitude of connection among nodes. The weighted inputs, then, generate an output to the output layers.
Where artificial network are used?
The development of artificial networks regulates some sectors such as health, finance, and more. The system that is developed resembles the human brain’s function. Here are some applications of the artificial network.
- Facial recognition – this application is essential in a surveillance system. Such technology should be able to recognize human faces then compare them with the data of digital images.
- Stock market prediction – predicting the stock market is almost impossible but the artificial network makes a huge change in the industry.
- Aerospace – this industry of aircraft and spacecraft employs the time-delay neural networks for various purposes.
- Social media – the artificial network is applied to social media to study the behavior of the users. The technology makes it easy to analyze the user’s behavior through daily conversations and posts on social media.
- Handwriting analysis – other than face recognition, an artificial network is also used to analyze handwriting and verification signatures.
What are the types of an artificial neural networks?
The artificial network computational models work similarly to the human nervous system. The technology is implemented in various fields. However, the base of the artificial network is the mathematical operational or known as algorithms. Here are some types of artificial networks.
- Feedforward neural network
- Radial basis neural network
- Kohonen self-organizing
- Recurrent neural network
- Convolutional neural network
- Modular neural network
What are the main components of artificial neural networks?
Since an artificial network is designed after the human brain, it has some main components which are neurons and bias. A single artificial network is made of hundreds or thousands of neurons connected. It is quite rare to find single networks since most developers choose multi-layer networks or stacked neural networks. Nodes build the network layers as well as the place of computation activities.
What is a multilayer neural network?
The multilayer neural network contains some artificial network layers or nodes. Each layer has a different design for a different purpose. In the early invention, it is possible to use a single layer of the artificial network but these days most technologies employ multilayer networks. For example, a convolutional neural network uses dozens of layers to recognize an image.
What is a backpropagation algorithm in a neural network?
Continuing the neural network, backpropagation refers to neural network training. It is the kind of method to tune the neural network according to the error rate from the previous epoch. Such a method allows you to minimize the error rate and improve the model reliability by increasing the generalization. In general, this is a method to train the artificial networks to reduce the loss function of the weights.
Download Artificial Neural Network PowerPoint Presentation:
What are neural networks best for?
Neural networks have diverse applications, from handwriting analysis, facial recognition, to weather forecasting. Lots of field uses artificial network including healthcare, finance, automotive, aerospace, robotics, telecommunication, and more. The neural network is simply changing how the world used to work for the better.
Examples of Artificial Neural Network
The neural network has been implemented in various fields. For example, the automotive industry takes advantage of artificial networks to improve virtual sensors, guidance systems, and so on. While the aerospace industry uses artificial networks to detect aircraft faults, improve aircraft control systems, and many more.
What are the advantages of artificial neural networks?
In general, the artificial network can learn and analyze data by themselves. The output from the network is not limited to only the input that they get. The technology does not affect data loss since the input data is not stored in a database but on the network itself. The system can recognize fault and give output even when the neuron does not respond or there is information missing. The good thing about the artificial network is the system can perform several tasks at once without affecting the performance of the system in general.
What are the disadvantages of an artificial neural network?
The artificial network does not come without drawbacks. There are some concerns regarding the technology such as hardware dependence, unexplained functioning, unknown duration, difficulty to show the problem, and more.
Few websites to refer:
The artificial neural network is the future of artificial intelligence. The technology has been implemented in various sectors to solve complex problems. The neural network applications are getting wider from simple face recognition to observing social media users’ behavior and finding fault in some applications.