Stated in its most basic form, the objective of damage diagnosis is to ascertain simply if damage is present or not based on measured dynamic characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and environmental and operational variations of the system can often mask subtle changes in ...

Prediction of software engineering variables with high accuracy is still an open problem. The primary reason for the lack of high accuracy in prediction might be because most models are linear in the parameters and so are not sufficiently flexible and suffer from redundancy. In this chapter, we focus on improving regression models by decreasing their redundancy and increasing their parsimony, i...

The dynamical behavior and the stability properties of fixed points in a feedback auto-associative memory are investigated. The proposed structure encompasses a multi-layer perceptron (MLP) and a feedback connection that links input and output layers through delay elements. The MLP is initially trained so that it maps the training patterns into themselves as an auto-associative memory. The feed...

This paper introduces the theory of factor analysis of the mixture of Auto-Associative Neural Networks (AANNs) with application in speaker verification. First, we formulate the problem of learning a low-dimensional subspace in part of the mixture of AANNs parameter space, and subsequently derive the update equations by minimizing loss function of the mixture. Second, we apply this technique to ...

Letter-to-sound (LTS) rules play a vital role in building a speech synthesis system. In this paper, we apply various Machine Learning approaches like Classification and Regression Trees (CART), Decision Forest, forest of Artificial Neural Network (ANN) and Auto Associative Neural Networks (AANN) for LTS rules. We used these techniques mainly for Schwa deletion in Hindi. We empirically show that...

A design procedure of neural associative memories to be used for robot vision systems is developed, which fits in the capabilities both of discrete-time cellular neural networks (DTCNNs) and fuzzy logic. The choice of this kind of neural networks is motivated by their architecture, suitable for storing images, and their locally connected structure, which is effective for the hardware implementa...

The reconstruction of the observer motion and environment structure from optic ow is considered for the case where the camera mapping is unknown. This mapping has therefore to be estimated from a given set of examples, the training set. Since this mapping is not a function in the sense of a many to one mapping, standard neural networks are unable to learn this mapping. We propose to represent t...

A Hopfield network is an auto-associative, distributive model of neural memory storage and retrieval. A form of error-correcting code, the Hopfield network can learn a set of patterns as stable points of the network dynamic, and retrieve them from noisy inputs – thus Hopfield networks are their own decoders. Unlike in coding theory, where the information rate of a good code (in the Shannon sens...

The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activi...