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.

Quality of child abuse information in emergency department injury surveillance data : linking hospital and child welfare data

Emergency departments (EDs) are often the first point of contact with an abused child. Despite legal mandate, the reporting of definite or suspected abusive injury to child safety authorities by ED clinicians varies due to a number of factors including training, access to child safety professionals, departmental culture and a fear of ‘getting it wrong’. This study examined the quality of documentation and coding of child abuse captured by ED based injury surveillance data and ED medical records in the state of Queensland and the concordance of these data with child welfare records. A retrospective medical record review was used to examine the clinical documentation of almost 1000 injured children included in the Queensland Injury Surveillance Unit database (QISU) from 10 hospitals in urban and rural centres. Independent experts re-coded the records based on their review of the notes. A data linkage methodology was then used to link these records with records in the state government’s child welfare database. Cases were sampled from three sub-groups according to the surveillance intent codes: Maltreatment by parent, Undetermined and Unintentional injury. Only 0.1% of cases coded as unintentional injury were recoded to maltreatment by parent, while 1.2% of cases coded as maltreatment by parent were reclassified as unintentional and 5% of cases where the intent was undetermined by the triage nurse were recoded as maltreatment by parent. Quality of documentation varied across type of hospital (tertiary referral centre, children’s, urban, regional and remote). Concordance of health data with child welfare data varied across patient subgroups. Outcomes from this research will guide initiatives to improve the quality of intentional child injury surveillance systems.

Vitamin D and injury prevention

Vitamin D, along with calcium, may help decrease the risk of falls and fractures in older adults. Sunlight and other sources of ultraviolet radiation are not recommended because they increase the risk of skin cancers and sun-induced eye disorders. Rather, vitamin D and calcium needs should be met through foods and dietary supplements. As a preventive measure to reduce the risk of falls and fractures, it is recommended that older adults meet the 2005 Dietary Guidelines and consume 1000 IU of vitamin D, preferably as vitamin D3.

The development of the extended adolescent injury checklist (E-AIC) : a measure for injury prevention program evaluation

The Extended Adolescent Injury Checklist (E-AIC), a self-report measure of injury based on the model of the Adolescent Injury Checklist (AIC), was developed for use in the evaluation of school-based interventions. The three stages of this development involved focus groups with adolescents and consultations with medical staff, pilot testing of the revised AIC in a high school context, and use of the finalised checklist in pre- and post-questionnaires to examine its utility. Results revealed that responses to the final version of the E-AIC were meaningful and remained consistent over time. The E-AIC appears to be a promising measure of adolescent injury that is simple, time-efficient and appropriate for use in the evaluation of school-based injury prevention programs.

Injuries across adolescence : an investigation using the Extended Adolescent Injury Checklist (E-AIC)

Injury is the leading cause of death among adolescents, and in many countries, accounts for more deaths than all other causes combined. Rates of death due to injury also increase dramatically across adolescence. The Australian Institute of Health and Welfare reported that, in 2005, there were 954 deaths of young Australians due to injury, which is a rate of 26 deaths per 100,000 young people. Of these deaths, 4% were adolescents aged 12-14, 17% were aged 15-17, and 80% were aged 18-24 years. Issues addressed: Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except schoolbased males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC. Findings suggest that the E-AIC is a useful measure that captures the injury experiences of adolescents in different contexts. The high occurrence of injuries that do not result in formal medical treatment also indicates scope for interventions to be based around lessons in first aid, while also incorporating injury prevention components.