ARTIFICIAL NEURAL NETWORKS

Introduction to Neural Networks

Neural 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 Networks Remarkable ability to derive meaning from complicated data. Used to extract patterns and detect complex trends. It can be compared to an expert. Advantages 1.Adaptive learning 2.Self organisation 3.Real time operations 4.Fault tolerance via redundant information coding.

Neural network versus Conventional 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 and Artificial Neurons

Learning 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 neurons 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 Approach

Artificial Neuron It is a device with many inputs and one output. Two modes of operation 1.Training mode 2.Using mode

Firing 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: X2: X3: OUT:000/1 1 1

The truth table after generalisation : X1: X2: X3: OUT:0000/1 111

Pattern 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: X12: X13: OUT: X21: X22: X23: OUT:10/ Top neuron Middle neuron

X21: X22: X23: OUT: Bottom neuron

From the tables following associations 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 Networks

Feed-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 Networks Signal travel in both directions. Are dynamic in nature. Used to denote feedback connections in single layer organisations.

Network 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 Networks

Neural 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 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 cardiovascular 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 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 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 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 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 Engineering 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 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.