Prestructuring Neural Networks via Extended Dependency Analysis with Application to Pattern Classification

We consider the problem of matching domain-specific statistical structure to neural-network (NN) architecture. In past work we have considered this problem in the function approximation context; here we consider the pattern classification context. General Systems Methodology tools for finding problem-domain structure suffer exponential scaling of computation with respect to the number of variables considered. Therefore we introduce the use of Extended Dependency Analysis (EDA), which scales only polynomially in the number of variables, for the desired analysis. Based on EDA, we demonstrate a number of NN pre-structuring techniques applicable for building neural classifiers. An example is provided in which EDA results in significant dimension reduction of the input space, as well as capability for direct design of an NN classifier

A neural network and rule based system application in water demand forecasting

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination

Autonomy in the real real-world: A behaviour based view of autonomous systems control in an industrial product inspection system

The thesis presented in this dissertation appears in two sequential parts that arose from an exploration of the use of Behaviour Based Artificial Intelligence (BBAI) techniques in a domain outside that of robotics, where BBAI is most frequently used. The work details a real-world physical implementation of the control and interactions of an industrial product inspection system from a BBAI perspective. It concentrates particularly on the control of a number of active laser scanning sensor systems (each a subsystem of a larger main inspection system), using a subsumption architecture. This industrial implementation is in itself a new direction for BBAI control and an important aspect of this thesis. However, the work has also led on to the development of a number of key ideas which contribute to the field of BBAI in general. The second part of the thesis concerns the nature of physical and temporal constraints on a distributed control system and the desirability of utilising mechanisms to provide continuous, low-level learning and adaptation of domain knowledge on a sub-behavioural basis. Techniques used include artificial neural networks and hill-climbing state-space search algorithms. Discussion is supported with examples from experiments with the laser scanning inspection system. Encouraging results suggest that concerted design effort at this low level of activity will benefit the whole system in terms of behavioural robustness and reliability. Relevant aspects of the design process that should be of value in similar real-world projects are identified and emphasised. These issues are particularly important in providing a firm foundation for artificial intelligence based control systems

Mapping the Substrate of Atrial Fibrillation: Tools and Techniques

Atrial fibrillation (AF) is the most common cardiac arrhythmia that affects an estimated 33.5 million people worldwide. Despite its prevalence and economic burden, treatments remain relatively ineffective. Interventional treatments using catheter ablation have shown more success in cure rates than pharmacologic methods for AF. However, success rates diminish drastically in patients with more advanced forms of the disease. The focus of this research is to develop a mapping strategy to improve the success of ablation. To achieve this goal, I used a computational model of excitation in order to simulate atrial fibrillation and evaluate mapping strategies that could guide ablation. I first propose a substrate guided mapping strategy to allow patient-specific treatment rather than a one size fits all approach. Ablation guided by this method reduced AF episode durations compared to baseline durations and an equal amount of random ablation in computational simulations. Because the accuracy of electrogram mapping is dependent upon catheter-tissue contact, I then provide a method to identify the distance between the electrode recording sites and the tissue surface using only the electrogram signal. The algorithm was validated both in silico and in vivo. Finally, I develop a classification algorithm for the identification of activation patterns using simultaneous, multi-site electrode recordings to aid in the development of an appropriate ablation strategy during AF. These findings provide a framework for future mapping and ablation studies in humans and assist in the development of individualized ablation strategies for patients with higher disease burden

Integrating the key approaches of neural networks

The thesis is written in chapter form. Chapter 1 describes some of the history of neural networks and its place in the field of artificial intelligence. It indicates the biological basis from which neural network approximation are made. Chapter 2 describes the properties of neural networks and their uses. It introduces the concepts of training and learning. Chapters 3, 4, 5 and 6 show the perceptron and adaline in feedforward and recurrent networks particular reference is made to regression substitution by "group method data handling. Networks are chosen that explain the application of neural networks in classification, association, optimization and self organization. Chapter 7 addresses the subject of practical inputs to neural networks. Chapter 8 reviews some interesting recent developments. Chapter 9 reviews some ideas on the future technology for neural networks. Chapter 10 gives a listing of some neural network types and their uses. Appendix A gives some of the ideas used in portfolio selection for the Johannesburg Stock Exchange.ComputingM. Sc. (Operations Research

ANALYSIS OF THE VOLTAGE STABILITY PROBLEM IN ELECTRIC POWER SYSTEMS USING ARTIFICIAL NEURAL NETWORKS

PhDThe voltage stability problem in electric power systems is concerned with the analysis of events and mechanisms that can lead a system into inadmissible operating conditions from the voltage viewpoint. In the worst case, total collapse of the system may result, with disastrous consequences for both electricity utilities and customers. The analysis of this problem has become an important area of research over the past decade due to some instances of voltage collapse that have occurred in electric systems throughout the world. This work addresses the voltage stability problem within the framework of artificial neural networks. Although the field of neural networks was established during the late 1940s, only in the past few years has it experienced rapid development. The neural network approach offers some potential advantages to the solution of problems for which an analytical solution is difficult. Also, efficient and accurate computation may be achieved through neural networks. The first contribution of this work refers to the development of an artificial neural network capable of computing a static voltage stability index, which provides information on the stability of a given operating state in the power system. This analytical tool was implemented as a self-contained computational system which exhibited good accuracy and extremely low processing times when applied to some study cases. Dynamic characteristics of the electrical system in the voltage stability problem are very important. Therefore, in a second stage of the present work, the scope of the research was extended so as to take into account these new aspects. Another neural network-based computational system was developed and implemented with the purpose of providing some information on the behaviour of the electrical system in the immediate future. Examples and case studies are presented throughout the thesis in order to illustrate the most relevant aspects of both artificial neural networks and the computational models developed. A general discussion summarises the main contributions of the present work and topics for further research are outlined.CNPq -Conselho Nacional de Desenvolvimento Cientffico e Tecnoldgico EPUSP -Escola Politecnica da Universidade de Sao Paul

Optimal placement of sensors to detect delamination in composite beams

The paper describes an approach for the optimal placement of sensors in composite beam structures for online detection of damage. The ability to identify damage is based on establishing a mapping between the charactgeristics of specific damage mechanisms (location and extent) such as delamination, fiber breakage, and matrix cracking, and strain measurements at the selected sensor locations; a trained neural network is proposed as a tool to generate this mapping. The design problem considered in the present paper was to place the least number of sensors in the structure so that the ability of the neural network to predict the extent and location of damage is not compromised. The optimization problem involved a mix of discrete and integer variables, and a genetic algorithm was used as the search tool. ~TRODUCTION A "smart" structure, instrumented with sensors and actuators, and responding in an intelligent manner to a dyoamically changing environment, is an intriguing concept. The key ingredients in the reafization of such a system include an adequate instrumentation of the structure, ability to rapidly analyze measured data and correlate to the existing state of the system, and to limit adverse structural behavior by providing real-time reaction in response to the evaluated state of the system. The present paper focusses on the use of strain measurements to detect delamination damage in composite beams. The approach can be extended to include other commonly encountered damage mechanisms in composites such as fiber breakage and matrix cracking, and analytical models relating the location and size of damage to strain fields in the structure under an applied load, are presented in (Teboub & Hajela, 1992). Since the damage can be in more than one place, and furthermore, there can be multiple modes of damage present at the same time, the identification space in such a problem is often nonunique. Artificial neural network (ANN) based classifiers present themselves as a logical tool for relating specific strain response to damage type and location. Once trained, these networks can rapidly generalize new strain measurements into an estimated state of the structure, and are therefore ideal for online damage detection systems. An adequate instrumentation of the structure, however, continues to be a pivotal problem. The least number of sensors is clearly desirable from a standpoint of complexity of hardware. However, a sufficient number must be placed to resolve problems of nonunique identification and to have a robust system that is relatively insensitive to partial failures in the sensor array. The problem of optimally locating the least number of sensors that would identify damage over some admissible range of degradation and location, is explored in subsequent sections of this paper. Placement of sensors at some predefined grid in the structure is a discrete optimization problem, and computationally burdensome to handle using traditional branch-and-bound methods in nonlinear programming. The use of a genetic algorithm (Hajela 1993) is adopted in the present work, as this method is naturally amenable to search in a discrete space. Once the placement of the sensors is known, a neural network can be trained to develop the mapping between the characteristics of damage, and the strain measurements at the sensor locations. However, to determine the optimal location of sensors by genetic search requires that a very large number of function evaluations be performed. Such function evaluations would involve determining the strain state for many different sizes and locations of damage, and then varying the number and locations of sensors to find the optimal distribution of the sensors in the structure to success_fully identify various occurrences of damage. This is clearly a computationally intensive procedure, and in the present work, a trained neural network was used as an approximate analysis tool. Note that in optimizing for sensor locations, each strain reading may correspond to a totally different set of sensor locations. A novel hybrid neural network and

Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity

By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification accuracy of >95%. This study shows that handheld NIR coupled with machine learning algorithms is a useful, fast, non-destructive tool to identify the authenticity of chicken meat. By comparing and combining different protocols to measure the NIR spectra (i.e., through packaging and directly on meat), we show the possibilities for both consumers and food inspection authorities to check the authenticity and origin of packaged chicken fillet.</p