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Have you ever spent time reading about AI (artificial intelligence)? If yes, then you have certainly heard about artificial neural networks (ANNs). However, do you not know exactly what artificial neural networks are? Rather than enrolling in any course or researching in-depth into any online resources, simply read the things mentioned in this article. It gives you more knowledge about the topic in a clear and precise manner.
What are Artificial Neural Networks?
ANNs is a computational model working built on the functions and structures of the biological model. As the flow of information affects the ANN structure, the neural network constantly changes depending on the input and output.
ANN is basically non-linear statistical data, which means a complex relationship describes input and output so that you obtain different patterns. This is it is called a neural network.
ANNs are one of the imperative tools utilized in machine learning. As the name suggests, ANNs is a brain-inspired system intended to imitate the way that humans learn.
It comprises of input and output layer along with the hidden layer, which contains units, which change the input into something, which will be used by the output layer.
This excellent tool is used for figuring out patterns that are too difficult or plentiful for the programmer (human) in order to extract as well as teach the system to recognize. Since the 1940s neural networks have been in use and it becomes a vital part of AI in the last few decades.
It is all because of the advent of the technique named back propagation. It actually lets networks adjust their neurons in the hidden layers when the outcome does not meet what the creator looking for.
This field has seen a huge rise after the arrival of the concept of deep learning in which dissimilar layers of the multilayer networks extract various features until finding what it is hoping for.
In simple words, the ANN is a try to stimulate the neuron networks, which make up the human brain, and therefore computers will be able to learn things and even make a decision like a human. Thus, ANNs are basically created to act like a human brain by using regular programming systems.
Understand the structure of ANNs
The basic idea behind the artificial neural network is the working of the human brain. It was limited to utilizing the wires and silicon as living dendrites and neurons.
The part of the human brain here is neurons, which are composed of eight-six billion-nerve cells. Additionally, they connected to other thousands of cells through Axons.
Plenty of inputs from the sensory organs are accepted by the dendrites. Finally, it creates the electric impulses, which are accessed to travel via ANN. Whenever you are intending to handle several problems neuron sends the message to another one.
Likewise, you can say ANNs comprised of several nodes, which emulate neurons present in the human brain. Even though you can connect these neurons through links, they can communicate with each other.
Nodes are usually accessed to take the input information and do a simple operation and then passed to other neurons to produce output at every node called node value or activation. Since every link is incorporated with some weight, they get the capability of learning.
Different types of artificial neural networks
In ANNs, plenty of types of neural networks are available and each of them comes along with its own benefits and level of complexity.
FeedForward neutral network
Feed Forward ANN is one of the basic types of neural set in which data travels in only one direction is from input to output. Additionally, there is no look present in this network.
Recurrent neural network
In this network type, data can flow in several directions and possess higher learning abilities. This network is widely utilized for several complex tasks including language recognition and learning handwriting.
FeedBack neutral network
This particular artificial neural network lets feedback loops and it is also accessed in the content-addressable memories.
Other artificial neural networks are Hopfield networks, convolutional neural networks, Boltzmann machine networks, etc. Based on the data you wish to train your system, choose the appropriate network.
Applications of artificial neural networks
ANNs are accessed to perform several tasks in several industries, which minimizes the workload of humans. Below mentioned are some of the major applications of ANNs in several industries.
- Aerospace–In the autopilot aircraft, artificial neural networks utilized for aircraft fault detection.
- Military–ANN is used in the military for several ways including target tracking, steering, and weapon orientation.
- Medical – Artificial neural network offered several machines for the medical field, which can be utilized in various ways. It includes ECG analysis, cancer cell analysis, and EEG.
- Speech–ANNs are accessed in speech classification and speech recognition
- Transportation –In the transportation field, ANN is widely used in several ways such as vehicle scheduling, brake system diagnosis, and routing systems
- Telecommunications –ANNs is used in transportation in several ways and some of them are vehicle scheduling, brake system diagnosis, and routing systems
- Time series prediction –Artificial neural network is used to make predictions on natural calamities and stocks.
- Software –Softwares such as facial recognition, pattern recognition, and optical character recognition are made from ANNs.
Benefits of Artificial Neural Networks (ANNs)
ANNs benefits are not ending here because it has several potentials to take up, which you will witness in the upcoming years.
- Artificial neural networks (ANNs) are adaptive and flexible
- It also accessed in data processing, modeling, sequence & pattern recognition systems, and so on
- It obtains knowledge from the surroundings by adapting to external and internal parameters so that solve the complex issues quickly and easily
- They have the ability to learn, simplify and adapt to any kind of situations according to their findings
- It is capable of higher fault tolerance when compared to traditional network
- It generalized knowledge to deliver adequate responses to many unknown situations
- It allows networks to obtain knowledge efficiently by leaning
Now, you have studied everything about Artificial Neural Networks including ANN structure, types, and applications. Artificial Neural Networks are of great use, however, they also have certain limitations. They need more time to be trained for working on complex challenges as well as more computing power.
Also, although ANNs is helpful in finding out the answer, it still lacks the decision-making ability. The research on enhancing their usability is in progress and soon, the world would get better ANNs to work upon.