Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659

Embedded expert system for space shuttle main engine maintenance

The SPARTA Embedded Expert System (SEES) is an intelligent health monitoring system that directs analysis by placing confidence factors on possible engine status and then recommends a course of action to an engineer or engine controller. The technique can prevent catastropic failures or costly rocket engine down time because of false alarms. Further, the SEES has potential as an on-board flight monitor for reusable rocket engine systems. The SEES methodology synergistically integrates vibration analysis, pattern recognition and communications theory techniques with an artificial intelligence technique - the Embedded Expert System (EES)

A question of balance: The benefits of pattern-recognition when solving problems in a complex domain

This is the accepted manuscript version of the following article: M. Lloyd-Kelly, F. Gobet, and Peter C. R. Lane, “A Question of Balance The Benefits of Pattern-Recognition when Solving Problems in a Complex Domain”, LNCS Transactions on Computational Collective Intelligence, Vol. XX, 2015. The final published version is available at: http://www.springer.com/gb/book/9783319275420 © 2015 Springer International Publishing.The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative,problem-solving system and a quicker, reactive, pattern-recognition system. We alter the balance of these systems in a number of computational simulations using three types of agent equipped with a novel, hybrid, human-like cognitive architecture. These agents are situated in the stochastic, multi-agent Tileworld domain, whose complexity can be precisely controlled and widely varied. We explore how agent performance is affected by different balances of problem-solving and pattern-recognition, and conduct a sensitivity analysis upon key pattern-recognition system variables. Results indicate that pattern-recognition improves agent performance by as much as 36.5 % and, if a balance is struck with particular pattern-recognition components to promote pattern-recognition use, performance can be further improved by up to 3.6 %. This research is of interest for studies of expert behaviour in particular, and AI in general.Peer reviewedFinal Accepted Versio

Measure Refutations and Metrics on Statements of Experts (Logical Formulas) in the Models for Some Theory

* This work was financially supported by the Russian Foundation for Basic Research, project no. 04-01-00858a.The paper discusses a logical expert statements represented as the formulas with probabilities of the first order language consistent with some theory T. Theoretical-models methods for setting metrics on such statements are offered. Properties of metrics are investigated. The research allows solve problems of the best reconciliation of expert statements, constructions of decision functions in pattern recognition, creations the bases of knowledge and development of expert systems

ADVANCES IN KNOWLEDGE DISCOVERY IN DATABASES

The Knowledge Discovery in Databases and Data Mining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. While Data Mining represents a set of specific algorithms of finding useful meanings in stored data, Knowledge Discovery in Databases represents the overall process of finding knowledge and includes the Data Mining as one step among others such as selection, pre�processing, transformation and interpretation of mined data. This paper aims to point the most important steps that were made in the Knowledge Discovery in Databases field of study and to show how the overall process of discovering can be improved in the future.

What makes trading strategies based on chart pattern recognition profitable?

[EN] Automating chart pattern recognition is a relevant issue addressed by researchers and practitioners when designing a system that considers technical analysis for trading purposes. This article proposes the design of a trading system that takes into account any generic pattern that has been proven to be profitable in the past, without restricting the search to the specific technical patterns reported in the literature, hence the term generic pattern recognition. A fast version of dynamic time warping, the University College Riverside subsequence search suite (called the UCR suite), is employed for the pattern recognition task in an effort to produce trading signals in realistic timescales. This article evaluates the significance of the relation between the system's profitability and (a) the pattern length, (b) the take-profit and stop-loss levels and (c) the performance consensus of past patterns. The trading system is assessed under the mean¿variance perspective by using 560 NYSE stocks. The results obtained by the different parameter configurations are reported, controlling for both data-snooping and transaction costs. On average, the proposed system dominates the market index in the mean¿variance sense. Although transaction costs reduce the profitability of the proposed trading system, 92.5% of the experiments are profitable if the analysis is reduced to the parameter values aligned with the technical analysisTsinaslanidis, P.; Guijarro, F. (2021). What makes trading strategies based on chart pattern recognition profitable?. Expert Systems. 38(5):1-17. https://doi.org/10.1111/exsy.12596S11738

The use of information technology in aquaculture management

The recent advances in information technology (IT) have had profound impacts on all walks of life and aquaculture is no exception. The growing importance of aquaculture as an alternative source of protein has further emphasized the need to adapt and develop advanced IT for the better management of aquaculture facilities as well as the regional planning for aquaculture development. It is the objective of this paper to review the use and potential prospects of IT in aquaculture management. The information technologies considered are instrumentation and process control, data management, computerized models, decision support systems, artificial intelligence and expert systems, image processing and pattern recognition, geographical information systems, and information centres and networks. The review includes a brief introduction of each of the aforementioned technologies, followed by a survey of their current application as well as their potential use in aquaculture management. Abstract The recent advances in information technology (IT) have had profound impacts on all walks of life and aquaculture is no exception. The growing importance of aquaculture as an alternative source of protein has further emphasized the need to adapt and develop advanced IT for the better management of aquaculture facilities as well as the regional planning for aquaculture development. It is the objective of this paper to review the use and potential prospects of IT in aquaculture management. The information technologies considered are instrumentation and process control, data management, computerized models, decision support systems, artificial intelligence and expert systems, image processing and pattern recognition, geographical information systems, and information centres and networks. The review includes a brief introduction of each of the aforementioned technologies, followed by a survey of their current application as well as their potential use in aquaculture management