What is Artificial Neural Network
Definition – Artificial neural networks are the most powerful learning models. They have the versatility to approximate a wide range of complex tasks that represent multi-dimensional input-output maps. There is also an adaptation capability in the nervous network, and noise can also perform strongly in the environment.
What is artificial neural network (ANN) – This is an information processing paradigm, which is inspired by the biological nervous system, such as the brain, process information. The main element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interactive simple processing elements (neurons) working together to solve specific problems. ANN, like people, learn by example. An ANN is configured through a learning process for a specific application, such as pattern recognition or data classification. Learning in biological systems involves adjusting the synaptic connection between neurons.
A neural network is a computing system based on the biological nervous network that creates the human brain. Neural networks are not based on a particular computer program written for it, but it can improve and improve its performance over time.
A neural network is made up of the collection of units or nodes called neurons. These neurons are connected to each other by a connection called synapses. Through synapses, a neuron can pass the signal or information to another neuron pass. The received neuron can receive the signal, can process it and give the next signal. The process continues until the output signal is produced.
Applications of Artificial Neural Networks
1. Computer Vision: Since the computer cannot be written to identify all the objects present in existence, its only way is to use the nerve network, as time passes, computers recognize the bases of their own new things Which can be learned first.
2. Pattern Recognition/Matching: It can be applied to find the stores of images to say, to say a face with a known face. Used in criminal investigations.
3. Natural language processing: A system that allows the computer to recognize human language spoken by progressively learning and listening over time
Types of Artificial Neural Networks
1. Feedforward Neural Networks:
The feed forward neural network, often called multilayer perceptron (MLP) (also called Deep FeedFavor) was the first and simplest type of neural network artificial neural network.
A. The simplest type of feedforward neural network is the perceptron (no hidden layers).
B. Infeed forms, neural network connections do not create a cycle between the nodes.
C. Feedforward neural network flow of information in input from input to only one direction.
2. A Recurrent Neural Network (RNN) is a part of artificial neural networks where the relationship between the nodes creates a guided graph with a sequence. It allows displaying temporary dynamic behavior for a time sequence. RNN is designed to recognize the sequential characteristics of a data and to use the pattern to predict the next potential scenario.
3. Deep Feed Forward Neural Networks (DFF): The long-term short-term memory (LSTM) network is an extension for recurrent neural networks, which basically extends their memory. A common LSTM unit is composed of one cell, one input gate, one output gate, and one forgotten gate.
4. Recurrent Neural Networks (RNN):
GRNN to D.F. Specht in 1991, this is a variation for the radial base neural network. Similar to the back-propagation neural network, normal regression neural network (GRNN) is also a good tool for function approximation in the modeling toolbox.
5. General Regression Neural Network (GRNN):
Sensory neural networks (CNN) are similar to normal neural networks, which are usually applied to analyze visual imagery. CNN is known for its ability to recognize the patterns present in the images.
Characteristics of Artificial Neural Networks
- In Artificial neural network have more neurons that work as a processing elements.
- With the help of weighted connections, all processing elements are connected.
- Implemented learning process to acquire knowledge.
- All data are distributed by connections.
- Artificial neural network work as mathematical model.
Advantages of Artificial Neural Network
There are many benefits to Artificial Neural Networks such as:
- Neural networks are capable to learn themselves, and give the result that have not limitation to input.
- Neural network no need to store the database because they store all data in their network.
- Neural network can apply the input any time when event arises, and capable to work with real time events.
- If any time piece of information loss then this network is able to produce the output with real time detection of fault.
- Neural network can execute couple of tasks in parallel without consuming more extra system resources.
- Having good capabilities to produce output if its networks have not full knowledge.
- Whole network works on the fault tolerant model.
- All memory of neural network is distributed nature.
- Having capabilities to make machine learning.
- Parallel processing ability.
Disadvantage of Artificial Neural Network
Now , we will explain various limitations of Artificial Neural Network like as:
- Neural network need multiple processor in parallel nature for performing all task. So entire network totally hardware dependent.
- Artificial neural network provides the not accurate result, so this is not trust able network.
- There are not any hard and fact rule for determine to entire structure of the neural network.
- In the artificial neural network having a hard task to displaying the problems to the network because this entire network works on the numerical information.
- The duration of the network is unknown.
- Neural network need to huge data for machine learning algorithm.
- it’s computationally more expensive.
- Have not scalability
- Not flexibilities
- Neural network not stable when to insert the large problem.