recently different approaches have been developed in the field of sensor fault diagnostics based on auto-associative neural network (aann). in this paper we present a novel algorithm called self reconstructing auto-associative neural network (s-aann) which is able to detect and isolate single faulty sensor via reconstruction. we have also extended the algorithm to be applicable in multiple faul...

The common problem of missing data in databases is being dealt with, in recent years, through estimation methods. Auto-associative neural networks combined with genetic algorithms have proved to be a successful approach to missing data imputation. Similarly, two new auto-associative models are developed to be used along with the Genetic Algorithm to estimate missing data and these approaches ar...

Memory plays a major role in Artificial Neural Networks. Without memory, Neural Network can not be learned itself. One of the primary concepts of memory in neural networks is Associative neural memories. A survey has been made on associative neural memories such as Simple associative memories (SAM), Dynamic associative memories (DAM), Bidirectional Associative memories (BAM), Hopfield memories,...

In this paper, an image restoration algorithm is proposed to identify nonlinear and noncausal blur funclon using artificial neural networks. Image and degradation processes include both linear and nonlinear phenomena. The proposed neural network model, which combines an adaptive auto-associative network with a random Gaussian process, is used to restore the blurred image and blur function, simu...

Face classification is an important area of research with many applications, including biometric security and searching face databases. This article describes an approach to verify faces using Auto-associative Neural Networks and Eigenbands fusion. In Eigenbands strategy each faces is divided in horizontal bands from which are extracted features using PCA. This method aims capture discriminativ...

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a modelbased approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component an...

Auto-Associative models cover a large class of methods used in data analysis, among them are for example the famous PCA and the auto-associative neural networks. In this paper, we describe the general properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto-Associative model in a Gaussian setting. We show that...

In section 2, a definition of “trend” is given. In section 3, it is shown how to detect a trend using an auto-associative neural network. Experimental methods and results are reported in sections 4 and 5, and concluding remarks are given in section 6. Abstract — This paper reports the results of a new neural network based trend detector. An auto-associative neural network was trained with the “...

Modal analysis is now mature and well accepted in the design of mechanical structures. It determines the vibration mode shapes and the corresponding natural frequencies. However, the validity of modal analysis is limited to structures showing a linear behaviour. In non-linear structural dynamics, it is well known that mode shapes are no longer useful for the characterization of the dynamic resp...