Analysis of Hybrid Neural Network for Improved Performance
Zainab KhalidAwan, Aamir Khan, Anam Iftikhar, Sadia Zahid, Anam Malik · International Journal of Computer Applications · 2012
In this paper we take a close look at the Hybrid Neural Network Model.Hybrid model is attained by combining two Artificial Neural Networks (ANNs).In which the first model is used to perform the feature extraction task and the second one performs prediction task.This paper explores the classifying ability of the proposed hybrid model.We analyze the performance of the model, which is a compound characteristic, of which the prediction accuracy is the most important component.If the prediction accuracy of the model can be increased it will result into enhanced performance of the model.The model that has been built is under the umbrella of pattern recognition and incorporates some of the data mining techniques.Kernel Principal Component Analysis (KPCA) has been implemented in the pre-processing stage for easier subsequent analysis.By the end of the paper, the key factors that enhance the accuracy of the model have been identified and their role explained.It also has been shown that single ANN model's performance deteriorates on an unseen problem much more as compared to the hybrid model.The aim is to provide a model having better performance and accuracy.The paper focuses on the real world applications of the model.