GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

The application of neural networks to induction machine control

Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Alleviating ‘overfitting’ via genetically-regularised neural network

A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN `over-fitting'. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results.

Diarrhoea risk factors in enterally tube fed critically ill patients : a retrospective audit

Objective: Diarrhoea in the enterally tube fed (ETF) intensive care unit (ICU) patient is a multifactorial problem. Diarrhoeal aetiologies in this patient cohort remain debatable; however, the consequences of diarrhoea have been well established and include electrolyte imbalance, dehydration, bacterial translocation, peri anal wound contamination and sleep deprivation. This study examined the incidence of diarrhoea and explored factors contributing to the development of diarrhoea in the ETF, critically ill, adult patient. ---------- Method: After institutional ethical review and approval, a single centre medical chart audit was undertaken to examine the incidence of diarrhoea in ETF, critically ill patients. Retrospective, non-probability sequential sampling was used of all emergency admission adult ICU patients who met the inclusion/exclusion criteria. ---------- Results: Fifty patients were audited. Faecal frequency, consistency and quantity were considered important criteria in defining ETF diarrhoea. The incidence of diarrhoea was 78%. Total patient diarrhoea days (r = 0.422; p = 0.02) and total diarrhoea frequency (r = 0.313; p = 0.027) increased when the patient was ETF for longer periods of time. Increased severity of illness, peripheral oxygen saturation (Sp02), glucose control, albumin and white cell count were found to be statistically significant factors for the development of diarrhoea. ---------- Conclusion: Diarrhoea in ETF critically ill patients is multi-factorial. The early identification of diarrhoea risk factors and the development of a diarrhoea risk management algorithm is recommended.

Probiotics and diarrhoea management in enterally tube fed critically ill patients : what is the evidence?

Objective: The aim of this literature review is to identify the role of probiotics in the management of enteral tube feeding (ETF) diarrhoea in critically ill patients.---------- Background: Diarrhoea is a common gastrointestinal problem seen in ETF patients. The incidence of diarrhoea in tube fed patients varies from 2% to 68% across all patients. Despite extensive investigation, the pathogenesis surrounding ETF diarrhoea remains unclear. Evidence to support probiotics to manage ETF diarrhoea in critically ill patients remains sparse.---------- Method: Literature on ETF diarrhoea and probiotics in critically ill, adult patients was reviewed from 1980 to 2010. The Cochrane Library, Pubmed, Science Direct, Medline and the Cumulative Index of Nursing and Allied Health Literature (CINAHL) electronic databases were searched using specific inclusion/exclusion criteria. Key search terms used were: enteral nutrition, diarrhoea, critical illness, probiotics, probiotic species and randomised clinical control trial (RCT).---------- Results: Four RCT papers were identified with two reporting full studies, one reporting a pilot RCT and one conference abstract reporting an RCT pilot study. A trend towards a reduction in diarrhoea incidence was observed in the probiotic groups. However, mortality associated with probiotic use in some severely and critically ill patients must caution the clinician against its use.---------- Conclusion: Evidence to support probiotic use in the management of ETF diarrhoea in critically ill patients remains unclear. This paper argues that probiotics should not be administered to critically ill patients until further research has been conducted to examine the causal relationship between probiotics and mortality, irrespective of the patient's disease state or projected prophylactic benefit of probiotic administration.