Artificial Neural Networks Applications and Algorithms

What is an Artificial Neural Network?

• Clustering
• Classification
• Pattern Recognition

What is a Neural Network?

The term ‘Neural’ has origin from the human (animal) nervous system’s basic functional unit ‘neuron’ or nerve cells present in the brain and other parts of the human (animal) body. A neural network is a group of algorithms that certify the underlying relationship in a set of data similar to the human brain. The neural network helps to change the input so that the network gives the best result without redesigning the output procedure. You can also learn more about ONNX in this insight.

What are the advantages and disadvantages of it?

The advantages of are listed below:

• A neural network can perform tasks that a linear program can not.
• When an element of the neural network fails, its parallel nature can continue without any problem.
• A neural network learns and reprogramming is not necessary.
• It can be implemented in any application.
• It can be performed without any problem.

The disadvantages of are described below:

• The neural network needs training to operate.
• The architecture of a neural network is different from the architecture of microprocessors. Therefore, emulation is necessary.
• Requires high processing time for large neural networks.

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What are the parts of Neuron and their Functions?

The typical nerve cell of the human brain comprises of four parts:

Function of Dendrite

It receives signals from other neurons.

Soma (cell body)

Axon Structure

When the sum reaches a threshold value, the neuron fires, and the signal travels down the axon to the other neurons.

Synapses Working

The point of interconnection of one neuron with other neurons. The amount of signal transmitted depends upon the strength (synaptic weights) of the connections. The connections can be inhibitory (decreasing strength) or excitatory (increasing strength) in nature. So, a neural network, in general, has a connected network of billions of neurons with a trillion of interconnections between them.

What is the difference between brain and computer?

What is the difference between Artificial Neural Networks (ANN) VS Biological Neural Networks (BNN)? 

Artificial Neural Networks with Biological Neural Network

• A neural network acquires knowledge through learning.
• A neural network's knowledge is a store within inter-neuron connection strengths known as synaptic weights.

Artificial Neural Network (ANN) Vs Biological Neural Network (BNN)

• The Biological Neural Network's dendrites are analogous to the weighted inputs based on their synaptic interconnection in it.
• The cell body is comparable to the artificial neuron unit in it, comprising summation and threshold unit.
• Axon carries output that is analogous to the output unit in the case of it. So, it is model using the working of basic biological neurons.

How does it works?

• It can be viewed as weighted directed graphs in which artificial neurons are nodes, and directed edges with weights are connections between neuron outputs and neuron inputs.
• The Artificial Neural Network receives information from the external world in pattern and image in vector form. These inputs are designated by the notation x(n) for n number of inputs.
• Each input is multiplied by its corresponding weights. Weights are the information used by the neural network to solve a problem. Typically weight represents the strength of the interconnection between neurons inside the Neural Network.
• The weighted inputs are all summed up inside the computing unit (artificial neuron). In case the weighted sum is zero, bias is added to make the output not- zero or to scale up the system response. Bias has the weight and input always equal to ‘1'.
• The sum corresponds to any numerical value ranging from 0 to infinity. To limit the response to arrive at the desired value, the threshold value is set up. For this, the sum is forward through an activation function.
• The activation function is set to the transfer function to get the desired output. There are linear as well as the nonlinear activation function.

What are the commonly used activation functions?

• Binary - The output has only two values, either 0 and 1. For this, the threshold value is set up. If the net weighted input is greater than 1, the output is assumed as one otherwise zero.
• Sigmoidal Hyperbolic - This function has an ‘S’ shaped curve. Here the tan hyperbolic function is used to approximate output from net input. The function is defined as - f (x) = (1/1+ exp(-????x)) where ???? - steepness parameter.

What are its various types ?

Neural Network Architecture Types

• Perceptron Model in Neural Networks
• Radial Basis Function Neural Network
• Multilayer Perceptron Neural Network
• Recurrent Neural Network
• Long Short-Term Memory Neural Network (LSTM)
• Hopfield Network
• Boltzmann Machine Neural Network
• Convolutional Neural Network
• Modular Neural Network
• Physical Neural Network

• Perceptron Model 

• Radial Basis Function 

• Multilayer Perceptron 

• Recurrent 

• Long Short-Term Memory Neural Network (LSTM)

• Hopfield Network

• Boltzmann Machine Neural Network

• Convolutional Neural Network

• Modular Neural Network

• Physical Neural Network

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Hardware Architecture for Neural Networks

• Software simulation in conventional computer
• A special hardware solution for decreasing execution time.

Learning Techniques

Training Algorithms 

• Gradient Descent Algorithm

• Back Propagation Algorithm

Learning Data Sets 

• Training Data Set

• Validation Set Approach

• Making Test Set

What are the Five Algorithms to Train a Neural Network?

• Hebbian Learning Rule
• Self - Organizing Kohonen Rule
• Hopfield Network Law
• LMS algorithm (Least Mean Square)
• Competitive Learning

What is the architecture of it?

• Input layer - It contains those units (Artificial Neurons) which receive input from the outside world on which the network will learn, recognize about, or otherwise process.
• Output layer - It contains units that respond to the information about how it learn any task.
• Hidden layer - These units are in between input and output layers. The hidden layer's job is to transform the input into something that the output unit can use somehow.

What are the Learning Techniques in Neural Networks?

Here is a list of Learning Techniques

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Offline Learning
• Online Learning

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

Here, the output value is unknown, but the network provides feedback on whether the output is right or wrong. It is Semi-Supervised Learning.

• Offline Learning

The weight vector adjustment and threshold adjustment are made only after the training set is shown to the network. It is also called Batch Learning.

• Online Learning

Learning and Development in Neural Networks

• Nine inputs from x1 - x9 and bias b (input having weight value 1) are fed to the network for the first pattern.
• Initially, weights are initialized to zero.
• Then weights are updated for each neuron using the formulae: Δ wi = xi y for i = 1 to 9 (Hebb’s Rule)
• Finally, new weights are found using the formulae:
• wi(new) = wi(old) + Δwi
• Wi(new) = [111-11-1 1111]
• The second pattern is input to the network. This time, weights are not initialized to zero. The initial weights used here are the final weights obtained after presenting the first pattern. By doing so, the network.
• The steps from 1 - 4 are repeated for second inputs.
• The new weights are Wi(new) = [0 0 0 -2 -2 -2 000]

What are the use-cases of it?

There are four broad use-cases of Neural Network

• Classification Neural Network
• Prediction Neural Network
• Clustering Neural Network
• Association Neural Network

Classification Neural Network

A Neural Network can be trained to classify a given pattern or dataset into a predefined class. It uses Feedforward Networks.

Prediction Neural Network

A Neural Network can be trained to produce outputs that are expected from a given input. E.g., - Stock market prediction.

Clustering Neural Network

The Neural network can identify a unique feature of the data and classify them into different categories without any prior knowledge of the data. Following networks are used for clustering -

• Competitive networks
• Adaptive Resonance Theory Networks
• Kohonen Self-Organizing Maps.

Association Neural Network

Train the Neural Network to remember the particular pattern. When the noise pattern is presented to the network, the network associates it with the memory's closest one or discards it. E.g., Hopfield Networks, which performs recognition, classification, and clustering, etc.

What are the applications of neural networks?

• Neural Network for Machine Learning
• Face Recognition using it
• Neuro-Fuzzy Model and its applications 
• Neural Networks for data-intensive applications

Neural Networks for Pattern Recognition

• Supervised classification - Given the input pattern is known as the member of a predefined class.
• Unsupervised classification - Assign pattern is to a hitherto unknown class.

• Data acquisition and preprocessing
• Data representation
• Decision Making

Approaches For Pattern Recognition

• Template Matching
• Statistical
• Syntactic Matching

• Multilayer Perceptron
• Kohonen SOM (Self Organizing Map)
• Radial Basis Function Network (RBF)

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Neuro-Fuzzy Model and its applications 

What is Fuzzy logic?

Fuzzy logic refers to the logic developed to express the degree of truthiness by assigning values between 0 and 1, unlike traditional boolean logic representing 0 and 1.

What is Fuzzy logic role in Neural networks?

Fuzzy logic and it have one thing in common. They can be used to solve pattern recognition problems and others that do not involve any mathematical model.

What are the applications of Neuro-Fuzzy Model?

Systems combining both fuzzy logic and neural networks are neuro-fuzzy systems. These systems (Hybrid) can combine the advantages of both it and fuzzy logic to perform in a better way. Fuzzy logic and it have been integrated to use in the following applications -

• Automotive engineering
• Applicant screening of jobs
• Control of crane
• Monitoring of glaucoma

Neural Network for Machine Learning

• Multilayer Perceptron (supervised classification)
• Back Propagation Network (supervised classification)
• Hopfield Network (for pattern association)
• Deep Neural Networks (unsupervised clustering)

Neural Networks for data-intensive applications

Face Recognition using Artificial Neural Networks

Face recognition entails comparing an image with a database of saved faces to identify the person in that input picture. It is a mechanism that involves dividing images into two parts; one containing targets (faces) and one providing the background. The associated assignment of face detection has direct relevance to the fact that images need to analyze and faces identified earlier than they can be recognized.

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What is learning rule in neural network?

The learning rule is a type of mathematical logic. It encourages to gain from the present conditions and upgrade its efficiency and performance. The learning procedure of the brain modifies its neural structure. The expanding or diminishing quality of its synaptic associations rely upon their activity. Learning rules in the Neural network:

• Hebbian learning rule: It determines how to customize the weights of nodes of a system.
• Perceptron learning rule: Network starts its learning by assigning a random value to each load.
• Delta learning rule: Modification in a node's sympatric weight is equal to the multiplication of error and the input.
• Correlation learning rule: It is similar to supervised learning.

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• Read more about Open Neural Network Exchange Advantages
• Explore here about Machine Learning Model Visualization