ARTIFICIAL NEURAL NETWORKS. Introduction to Neural Networks.

ARTIFICIAL ARTIFICIAL NEURAL NEURAL

NETWORKSNETWORKS

Introduction to Neural Introduction to Neural NetworksNetworks

Neural networkNeural network

• It is an information processing paradigm

• It is based on the way in which biological nervous system works.

• It helps in processing information.

• e.g. ANN

Use of Neural NetworksUse of Neural Networks• Remarkable ability to derive meaning

from complicated data.• Used to extract patterns and detect

complex trends.• It can be compared to an expert.• Advantages1. Adaptive learning2. Self organisation3. Real time operations4. Fault tolerance via redundant

information coding.

Neural network versus Neural network versus Conventional ComputersConventional Computers

• Conventional computers use algorithmic approach i.e. computer follows a set of instructions in order to solve a problem which is in a way limit to solving capability.

• Neural networks process information like our brain does.

• Neural networks and conventional computers are not in competition but complement to each other.

Similarities between human Similarities between human and Artificial Neuronsand Artificial Neurons

Learning of a Human BrainLearning of a Human Brain

• The structure of a Human Neuron is shown below

When a neuron receives excitatory inputs larger than inhibitory input it sends an electrical activity down its axon to the synapses and thus the communication between various neurons exists.

From human neurons to artificial From human neurons to artificial neuronsneurons

• First we deduce essential features of neurons and their interconnection.

• Secondly, we program a computer to stimulate these features .

• Finally model achieved is a gross idealisation of real networks of neurons.

An Engineering ApproachAn Engineering Approach

Artificial NeuronArtificial Neuron

• It is a device with many inputs and one output.• Two modes of operation

1. Training mode

2. Using mode

Firing RuleFiring Rule

• Important concept accounting for high flexibility in neural network.

• Firing rule can be implemented using hamming distance technique.

• Firing rule applied to a 3 - input neuron.

X1: 0 0 0 0 1 1 1 1

X2: 0 0 1 1 0 0 1 1

X3: 0 1 0 1 0 1 0 1

OUT: 0 0 0/1 0/1 0/1 1 0/1 1

• The truth table after generalisation :

X1: 0 0 0 0 1 1 1 1

X2: 0 0 1 1 0 0 1 1

X3: 0 1 0 1 0 1 0 1

OUT: 0 0 0 0/1 0/1 1 1 1

Pattern RecognitionPattern Recognition

• An important application of neural networks

• can be implemented using a feed forward neural network that has been trained accordingly.

• Example: The figure is trained to recognize the following patterns:

The truth table for 3-neurons after generalisation

X11: 0 0 0 0 1 1 1 1

X12: 0 0 1 1 0 0 1 1

X13: 0 1 0 1 0 1 0 1

OUT: 0 0 1 1 0 0 1 1

X21: 0 0 0 0 1 1 1 1

X22: 0 0 1 1 0 0 1 1

X23: 0 1 0 1 0 1 0 1

OUT: 1 0/1 1 0/1 0/1 0 0/1 0

Top neuron

Middle neuron

X21: 0 0 0 0 1 1 1 1

X22: 0 0 1 1 0 0 1 1

X23: 0 1 0 1 0 1 0 1

OUT: 1 0 1 1 0 0 1 0

Bottom neuron

From the tables following From the tables following associations can be extractedassociations can be extracted

• Conclusion-The output is black and the total output of the network is still in favour of the “T” shape.

Architecture of Neural Architecture of Neural NetworksNetworks

Feed-forward NetworksFeed-forward Networks

• Allow the signal to travel in one direction.• Are straight forward networks that associate

inputs with outputs.• Extensively used in pattern recognition.

Feedback NetworksFeedback Networks• Signal travel in both directions.• Are dynamic in nature.• Used to denote feedback connections in

single layer organisations.

Network LayersNetwork Layers

• Three units-input, hidden, output.

• Activities of these units.

• Simple network is interesting because of hidden layers.

• Single and multi-layer architectures.

Applications of Neural Applications of Neural NetworksNetworks

Neural Networks in PracticeNeural Networks in Practice

• They are best suited for prediction or forecasting including: industrial process control, data validation, risk management, etc.

• Also used in specific paradigms: interpretation of multi meaning, texture analysis, facial recognition, recognition of speakers in communications ,etc.

Neural Networks in medicineNeural Networks in medicine

• The research on modeling parts of the human body and recognizing diseases from various scans.

• Used effectively in recognizing diseases as no details are needed to how to recognize the and no specific algorithm need to be provided.

Modelling and diagonising the Modelling and diagonising the cardiovascular systemcardiovascular system

• Potential harmful medical conditions can be detected at early stage using artificial cardiovascular system models.

• Ann technology is used as it provides sensor fusion which is combining of several values from different sensors

Electronic NosesElectronic Noses

• Neural networks have made possible to transmit various odours over long distances via communication links.

• This has help in enhancing telemedicine and telepresent surgery.

Instant PhysicianInstant Physician

• An associative neural network to store a large number medical records including symptoms,diagnosis,and treatment of specific case.

• After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the "best" diagnosis and treatment.

Neural Networks in BusinessNeural Networks in Business

• Any neural network application would fit into one business area or financial analysis.

• Neural networks is used for dataminig purposes, for various business purposes including resource allocation and scheduling.

Enhancing TradingEnhancing Trading

• The identification of specific patterns in stock price derived from technical stock analysis heuristics, which after occurring results in a predefined price movement.

• Neural networks are trained in the experiments to classify whether the outcome of an occurred pattern will result in a predefined price movement.

ANNs in Water Supply ANNs in Water Supply EngineeringEngineering

• Whenever this technology is applied for water supply engg. problems have reported findings that were beyond the capability of traditional statistical / mathematical modeling tools.

• Some of the applications performed includes: Forecasting salinity levels in River Murray, South Australia; Predicting gastroenteritis rates and waterborne outbreaks; Modeling pH levels in a eutrophic Middle Loire River, France;

Understanding Brain ActivityUnderstanding Brain Activity

• It provides a powerful new approach for neuroscience to study and manipulate signal propagation in neuronal networks

• It represents a new, powerful, and flexible approach for real-time cellular assays useful for drug discovery and other applications; and it opens the possibility for hybrid circuits that couple the strengths of digital nanoelectronic and biological computing components.