Fuzzy Neuro Systems for Machine Learning for Large Data Sets

Artificial Neural Networks have found a variety of applications that cover almost every domain. The increasing use of Artificial Neural Networks and machine learning has led to a huge amount of research and making in of large data sets that are used for training purposes. Handwriting recognition, speech recognition, speaker recognition, face recognition are some of the varied areas of applications of artificial neural networks. The larger training data sets are a big boon to these systems as the performance gets better and better with the increase in data sets. The higher training data set although drastically increases the training time. Also it is possible that the artificial neural network does not train at all with the large data sets. This paper proposes a novel concept of dealing with these scenarios. The paper proposes the use of a hierarchical model where the training data set is first clustered into clusters. Each cluster has its own neural network. When an unknown input is given to the system, the system first finds out the cluster to which the input belongs. Then the input is processed by the individual neural network of that system. The general structure of the algorithm is similar to a hybrid system consisting of fuzzy logic and artificial neural network being applied one after the other. The system has huge applications in all the areas where Artificial Neural Network is being used extensively.

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