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In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel data selection algorithm for identifying significant training data is presented for power quality (PQ) events classification. The key concept of this paper is to reduce the execution time, computational complexity and enhance the accuracy of the existing PQ classification system by reducing the number of support vectors. The proposed scheme identifies important training data and rejects redundant and irrelevant ones using a novel fuzzy C-means clustering-based data selection algorithm thereby reducing time of classification and enhancing accuracy. Significant features from raw PQ data are extracted using discrete wavelet transform and important training data are recognized using these features with the proposed data selection algorithm. Two machine learning algorithms namely the probabilistic neural network and support vector machine are employed and the best PQ classifier is investigated. Furthermore, the proposed data selection algorithm is integrated with the existing PQ classification system that has selected optimal input features and parameters of the classifier using Simulated Annealing and is shown to perform exceptionally well when compared to conventional classifiers that use full training data set. The suitability of the algorithm for noisy as well as real time data is examined and the empirical results show that the proposed scheme performs well compared to several existing PQ classification works. [ABSTRACT FROM AUTHOR]