Artificial Neural Network

Topics: Machine learning, Artificial neural network, Data mining Pages: 8 (2115 words) Published: August 25, 2013
Artificial Neural Network
Sphoorti Sood1and Divya Gupta2
1{Student of Computer Science Department SRMSWCET, Bareilly}
2{Student of Computer Science Department SRMSWCET, Bareilly}

Abstract— Artificial neural networks (ANNs) are simplified models of human brain. These are networks of computing elements that have the ability to respond to input stimuli and generate the corresponding output. To obtain a desirable output, the network weights must be trained upon the available data many times. Hence the software realization of ANN takes many hours to learn a particular example .This paper include the several advancement of ANN in cluster which would help us in the study of data mining, data compression, exploratory analysis or other aspect related to cluster. The cluster help ,us in the pattern matching that explore the cluster algorithm and place the similar pattern in the cluster. It highlights the important issues related and shows the possible direction of future research.

Keywords- Artificial neural network, Data clustering ,data compression, data mining ,exploratory analysis, pattern matching.

The modern usage of the term neural network is refers to artificial neural networks, which are composed of artificial neurons or nodes Artificial neural networks are composed of interconnecting artificial neurons. These neurons are connected through a network structure. Once a network has been structured for a particular application, that network is ready to be trained. We know that there are several learning methods for the training to that network like: Supervised Learning, Unsupervised Learning, Reinforced Learning etc. Through this approach we can train the computing data that are represented in a structure or network, often tabular , a tree or a graph structure. Clustering is used to organize that observe data into a meaningful structure. Clustering is the unsupervised learning of patterns (observations, data items or feature vectors) into groups (clusters). The system learns of its own by discovering and adapting to structural features in the input patterns. Clustering is useful in several exploratory pattern-analysis, data compressions, grouping , decision-making, data mining (document retrieval) and exploratory analysis . However in such a problem, there is little prior information (e.g., statistical models) available about the data, and the decision-maker must make as few assumptions about the data as possible .It is under these restrictions that clustering methodology is particularly appropriate for the exploration of inter relationships among the data points to make an assessment (perhaps preliminary) of their structure.

There are number of ways of defining the ANN. But the most appropriate definition are define as follows: “Artificial Neural Network are massively interconnected networks in parallel of simple elements (usually adaptable), with hierarchic organization, which try to interact with the object of the real world in the same way that the biological nervous system does.” As a simple element we understand the artificial equivalent of a neuron that is known as computational neuron or node or unit .these neurons are organized hierarchically by layers and are interconnected between them just as in the biological nervous systems. Upon the presence of an external stimulus the artificial neural network generates an answer, which is confronted with the reality to determine the degree of adjustment that is required in the internal network parameters. This adjustment is known as learning network or training, after which the network is ready to answer to the stimulus in an optimum way.

ANNs can be viewed as weighted directed graphs in which artificial neurons are nodes...

References: [5] M.J. Zurada, "Introduction to Artificial Neural
System Jacbio Publishing Mouse”, New Delhi(1999).
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