Noise Suppression By Artificial Neural Networks

Abstract

In the research field of Artificial Neural Networks, there are ongoing efforts to address the issue of noise corruption in patterns. Various approaches and techniques have been explored to suppress noise and improve the accuracy of pattern classification. One of the research themes in this direction is the theory of Support Vector Machines [52] (SVM). SVMs aim to find an optimal hyperplane that maximizes the margin between classes, effectively reducing the impact of noise on classification. This approach formulates the problem of noise suppression as a quadratic programming problem. Hopfield Associative Memory [5] is another research area that has been investigated for noise suppression. The concept of null vectors of perceptron’s, which refers to vectors in the null space of the perceptron's transformation matrix, is introduced and utilized to suppress noise. By studying the null vectors of Extreme Machine Learning [49], researchers have explored their potential for noise suppression in pattern classification tasks. In addition to these approaches, the idea of stacking associative memories has been utilized to develop Hopfield neural networks [5] capable of suppressing noise in different dimensions. This includes 1-D, 2-D, and 3-D Hopfield neural networks that are designed to effectively handle noise corruption in patterns of various dimensions. Furthermore, to further enhance classification accuracy by suppressing noise, a real-valued Hopfield neural network based on a novel model of artificial neuron called the ceiling neuron has been proposed and studied. This innovation aims to improve noise suppression capabilities and overall performance in pattern classification tasks. Overall, this thesis explores and innovates different approaches to suppress noise in pattern classification using various neural network models, such as Support Vector Machines, Hopfield Associative Memory, and real-valued Hopfield neural networks based on ceiling neurons. These efforts contribute to the ongoing research in noise suppression techniques and aim to improve the accuracy and reliability of pattern classification systems.