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...

This section considers how neural networks can be used as associative memory devices. It first describes what an associative memory is, and then moves on to describe associative memories based on feedforward neural networks and associative memories based on recurrent networks. The section also describes associative memory systems based on cognitive models. It also highlights the ability of neur...

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,...

We consider application of neural associative memories to chemical image recognition. Chemical image recognition is identification of substance using chemical sensors' data. The primary advantage of associative memories as compared with feed-forward neural networks is highspeed learning. We have made experiments on odour recognition using hetero-associative and modular autoassociative memories....

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...