Artificial Neural Network: Usage

Topics: Brain, Machine learning, Neuron Pages: 6 (1608 words) Published: September 11, 2013

Neuron Transfer Function:
The transfer function of a neuron is chosen to have a number of properties which either enhance or simplify the network containing the neuron. Crucially, for instance, any multilayer perceptron using a linear transfer function has an equivalent single-layer network; a non-linear function is therefore necessary to gain the advantages of a multi-layer network. Types of neuron transfer function •Pure Linear Transfer Function

Hard Limit Transfer Function
Log Sigmoid Transfer Function
Neural Network Structures:
Feed Forward Network - Information flow is unidirectional, information processing is parallel, memory less, cannot modify output based on error •Recurrent ( or feedback) Network – Information travelling in both directions, learn from mistakes, dynamic in nature •Feed Forward Back Propagation Network

How ANN Functions?
ANN functions through learning. Like human beings, ANN works by learning from its past experiences and mistakes. ANN is inspired by the learning processes that take place in biological systems.

utomating this are:
Saving quality time
Saving resources in terms of human resources and finance •Eliminating human error
Leveraging technology
Having fairness in the process
The framework proposed extracts data from various data sets. For example, while recruiting employees in IT industries, the typical process is to take an aptitude test, followed by an interview process. It also takes into account the marks obtained in the engineering semesters. However, the weight age and pattern of marks varies from college to college within the country. Supposing 100 applicants apply for 1 post gives us the ratio 1:100 and the number of candidates called for interview after the screening is 30, 20 or 10. Lesser the number of interviewees, more efficient is the process. Thus ANN should aim to reduce this ratio.

Data mining is the important approach to realize knowledge discovery. It is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data.

It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

(ANN) is one of the most efficient techniques of data mining. It is the nonlinear auto -fit dynamic system made of many cells with simulating the construction of many cells with simulating the construction of biology neural systems. ANN has the ability to mapping high nonlinear system, associable memory and abstractly generalization. It can make model from analyzing the mode in the data and discover the unknown knowledge.

Traditional Human Brain (THB) Vs. ANN approach in information processing: •THB actually treats the brain as a black box and simulates the human reasoning process. ANN also follows a similar process. It takes human brain as a model for reference and tries to create a system that can function similarly. •THB works sequentially i.e. in series whereas ANN works in parallel •THB acquires knowledge from outside and it gets coded inside whereas knowledge & learning is within the design system of ANN •THB functions in a deductive nature, using a generalised knowledge (common sense) but ANN is inductive in nature, creating a knowledge base from the newly acquired data, making a decision out of it •THB represents knowledge in an explicit form but ANN stores knowledge as an interconnection among its neurons

ANN does the work of data mining...
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