A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010

Analysis of a Gibbs sampler method for model based clustering of gene expression data

Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model based clustering approaches have emerged as statistically well grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. We have extended an existing algorithm for model based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for S. cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression.Comment: 8 pages, 7 figure

A fuzzy dynamic bayesian network-based situation assessment approach

Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems' accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach. © 2013 IEEE

An intelligent situation awareness support system for safety-critical environments

Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of four major elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level. © 2014 Elsevier B.V. All rights reserved