An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
Why use neural networks?
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
Other advantages include:
Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Architecture of neural networks
Feed-forward ANNs (figure 1) allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down.
Feedback networks (figure 1) can have signals travelling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organisations.
Applications of neural networks
Neural Networks in Practice
Given this description of neural networks and how they work, what real world applications are they suited for?
Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
industrial process control
But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.
Neural networks in medicine
Neural Networks in business
In order to obtain useful information for your assignment you need to identify what kind of information you need. Different kinds of information can be found in different types of resources. The kind of information you need will then determine which resource is the most appropriate.
Types of Information:
Different sorts of questions require different types of information to answer. In order to gather evidence to support an argument, you first need an idea of what types of information are suitable. You can gain a sense of which types of information are appropriate for your project by answering the questions in this section.
• What is my assignment and what are my opportunities for research?
In order to research effectively you need a solid understanding of what sort of evidence your assignment requires and what is available.
• Which academic disciplines does my research touch upon?
Research papers are usually written with the goal of contributing to the dialogue of a particular discipline. To do so, a paper must follow the standards of research and evidence for that discipline.
Description for different types of Information as follows:
Primary, Secondary and Tertiary Information: Most information is generally divided into three main categories: Primary ,Secondary and Tertiary.
Primary Information: Original material that has not been interpreted or analysied. Examples: Statistics, Research articles, Blogs, Websites
Secondary Material: Created from primary material, interpretating original material. Examples: Texbooks, Review articles
Tertiary Material: Acts as a tool in understanding and locating information. Examples: Databases, Subject Gateways, Dictionaries, Bibliographies
Different types of Information Systems:
Information systems are constantly changing and evolving as technology continues to grow. Very importantly the information systems described below are not mutually exclusive and some (especially Expert Systems, Management Information Systems and Executive Information Systems are can be seen as a subset of Decision Support Systems). However these examples are not the only overlaps and the divions of these information systems will change over time.
At present there are five main types:
• Transaction Processing Systems (TPS)
• Decision Support Systems (DSS)
• Expert Information Systems (EIS)
• Management Information Systems (MIS)
• Office Automation Systems (OAS)