ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.
Attributes of Neural Networks
With the human-like ability to problem-solve and apply that skill to huge datasets neural networks possess the following powerful attributes:
Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
Tasks Neural Networks Perform
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
Classification: NNs organize patterns or datasets into predefined classes.
Prediction: They produce the expected output from given input.
Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.
Network engineering applications currently in use in various industries:
- Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
- Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
- Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
- Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
- Mechanics: Condition monitoring, systems modeling, and control
- Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
- Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition).
Types of Neural Networks in Artificial Intelligence
|Based on the connection pattern||FeedForward, Recurrent||Feedforward: In which graphs have no loops. Recurrent: Loops occur because of feedback.|
|Based on the number of hidden layers||Single-layer, Multi-Layer||Single Layer: Having one secret layer. E.g., Single Perceptron Multilayer: Having multiple secret layers. Multilayer Perceptron|
|Based on the nature of weights||Fixed, Adaptive||Fixed: Weights are a fixed priority and not changed at all. Adaptive: Updates the weights and changes during training.|
|Based on the Memory unit||Static, Dynamic||Static: Memoryless unit. The current output depends on the current input. E.g., Feedforward network. Dynamic: Memory unit – The output depends upon the current input as well as the current output. E.g., Recurrent Neural Network|
Perceptron Model in Neural Networks
Neural Network is having two input units and one output unit with no hidden layers. These are also known as ‘single-layer perceptrons.’
Radial Basis Function Neural Network
These networks are similar to the feed-forward Neural Network, except radial basis function is used as these neurons’ activation function.
Multilayer Perceptron Neural Network
These networks use more than one hidden layer of neurons, unlike single-layer perceptron. These are also known as Deep Feedforward Neural Networks.
Recurrent Neural Network
Type of Neural Network in which hidden layer neurons have self-connections. Recurrent Neural Networks possess memory. At any instance, the hidden layer neuron receives activation from the lower layer and its previous activation value.
Long Short-Term Memory Neural Network (LSTM)
The type of Neural Network in which memory cell is incorporated into hidden layer neurons is called LSTM network.
A fully interconnected network of neurons in which each neuron is connected to every other neuron. The network is trained with input patterns by setting a value of neurons to the desired pattern. Then its weights are computed. The weights are not changed. Once trained for one or more patterns, the network will converge to the learned patterns. It is different from other Neural Networks.
Boltzmann Machine Neural Network
These networks are similar to the Hopfield network, except some neurons are input, while others are hidden in nature. The weights are initialized randomly and learn through the backpropagation algorithm.
Convolutional Neural Network
Get a complete overview of Convolutional Neural Networks through our blog Log Analytics with Machine Learning and Deep Learning.
Modular Neural Network
It is the combined structure of different types of neural networks like multilayer perceptron, Hopfield Network, Recurrent Neural Network, etc., which are incorporated as a single module into the network to perform independent subtask of whole complete Neural Networks.
Physical Neural Network
In this type of Artificial Neural Network, electrically adjustable resistance material is used to emulate synapse instead of software simulations performed in the neural network.