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 increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety applications for Earth observation monitoring. At same time, it requires new techniques that are able to manage amount data stored transmitted ground. Advanced on-board processing answer this problem, offering possibility select only interest specific applica...

The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information.

Inspired by nature, arti cial neural networks are an attempt to create a learning machine that is comparable to real brains. Due to the complexity of brains, and a lack of understand there of, arti cial neural networks are only remotely related to their natural counterparts. Also, in practice it has proven intractable to work with arbitrarily large neural networks as they are hard to control an...

Interpreting the information hidden in multidimensional data can be considered as a challenging and also a complicated task. The compression, dimension reduction and visualization of these multidimensional data provide ways to better understanding and interpretation of the problem. Usually, dimension reduction or compression is considered as the first step to data analysis and exploration. Here...

This paper presents methods which are aimed at finding approximations to missing data in a dataset by using optimization algorithms to optimize the network parameters after which prediction and classification tasks can be performed. The optimization methods that are considered are genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), random forest (RF) and negativ...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Biologically inspired neural networks which perform temporal sequence learning and generation are frequently based on heteroassociative memories. Recent work by Jensen and Lisman has suggested that a model which connects an auto-associator module to a hetero-associator module can perform this function. We modify this architecture in a simplified model which in contrast uses a pair of connected ...