630 research outputs found

    Improving the performance of cascade correlation neural networks on multimodal functions

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    Intrinsic qualities of the cascade correlation algorithm make it a popular choice for many researchers wishing to utilize neural networks. Problems arise when the outputs required are highly multimodal over the input domain. The mean squared error of the approximation increases significantly as the number of modes increases. By applying ensembling and early stopping, we show that this error can be reduced by a factor of three. We also present a new technique based on subdivision that we call patchworking. When used in combination with early stopping and ensembling the mean improvement in error is over 10 in some cases

    A study of early stopping, ensembling, and patchworking for cascade correlation neural networks

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    The constructive topology of the cascade correlation algorithm makes it a popular choice for many researchers wishing to utilize neural networks. However, for multimodal problems, the mean squared error of the approximation increases significantly as the number of modes increases. The components of this error will comprise both bias and variance and we provide formulae for estimating these values from mean squared errors alone. We achieve a near threefold reduction in the overall error by using early stopping and ensembling. Also described is a new subdivision technique that we call patchworking. Patchworking, when used in combination with early stopping and ensembling, can achieve an order of magnitude improvement in the error. Also presented is an approach for validating the quality of a neural network’s training, without the explicit use of a testing dataset

    Seed phosphorus : its effect on plant production

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    The amount of phosphorus in the seed of annual crop and pasture species influences production of plants grown from that seed. It appears the more phosphorus there is in the seed, the better the potential yield irrespective of whether fertilizer phosphorus is applied to the soil or not. This article discusses the influence of phosphorus concentration in the seed of annual crop and pasture species on subsequent production. It also explains the difference between phosphorus concentration and phosphorus content in the see

    Multi-objective robust topology optimization with dynamic weighting

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    A common robust topology optimization is formulated as a weighted sum of expected and variance of the objective functions for the given uncertainties. This has recently been applied to topology optimization with uncertainties in loading, [1]. Figure 1(a) shows the Pareto front of solutions found using uniformly distributed weightings. This front suffers from crowding for weight values 0.625. In the general case, the two goals of multi-objective optimization are; to find the most diverse set of Pareto optimal solutions, and, to discover solutions as close as possible to the true Pareto front. This paper presents schemes to achieve both these goals

    Self-organising symbolic aggregate approximation for real-time fault detection and diagnosis in transient dynamic systems

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    The development of accurate fault detection and diagnosis (FDD) techniques are an important aspect of monitoring system health, whether it be an industrial machine or human system. In FDD systems where real-time or mobile monitoring is required there is a need to minimise computational overhead whilst maintaining detection and diagnosis accuracy. Symbolic Aggregate Approximation (SAX) is one such method, whereby reduced representations of signals are used to create symbolic representations for similarity search. Data reduction is achieved through application of the Piecewise Aggregate Approximation (PAA) algorithm. However, this can often lead to the loss of key information characteristics resulting in misclassification of signal types and a high risk of false alarms. This paper proposes a novel methodology based on SAX for generating more accurate symbolic representations, called Self-Organising Symbolic Aggregate Approximation (SOSAX). Data reduction is achieved through the application of an optimised PAA algorithm, Self-Organising Piecewise Aggregate Approximation (SOPAA). The approach is validated through the classification of electrocardiogram (ECG) signals where it is shown to outperform standard SAX in terms of inter-class separation and intra-class distance of signal types

    Self-Organizing Piecewise Aggregate Approximation algortihm for intelligent detection and diagnosis of heart conditions

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    Electrocardiogram (ECG) signal classification is a recognized method for automated detection and diagnosis of heart abnormalities. This is typically achieved through dimensionality reduction techniques and feature extraction followed by signal classification using various machine learning algorithms. Although some algorithms can yield accurate results, they can be computationally demanding meaning that mobile analysis is difficult. Furthermore, discrete changes in signal characteristics, often exhibited as an early indication of the onset of heart abnormalities, can be lost in the dimensionality reduction process leading to misclassification of signal types. This paper presents a new dimensionality reduction algorithm, based on Piecewise Aggregate Approximation (PAA), called Self-Organizing Piecewise Aggregate Approximation (SOPAA) that is able to determine optimum PAA parameters based on signal characteristics within individual ECG data sets. This leads to more accurate and compact representations of ECG signals, improved classification of signal types and improved abnormality detection and diagnosis. In this work, ECG data from 99 patients exhibiting 3 different heart conditions are analyzed. Signals are discretized using both PAA and SOPAA and classified using the k-means clustering algorithm. It is shown that the SOPAA algorithm outperforms standard PAA by correctly classifying 19.7% more patients

    Goal driven optimization of process parameters for maximum efficiency in laser bending of advanced high strength steels

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    Laser forming or bending is fast becoming an attractive option for the forming of advanced high strength steels (AHSS), due primarily to the reduced formability of AHSS when compared with conventional steels in traditional contact-based forming processes. An inherently iterative process, laser forming must be optimized for efficiency in order to compete with contact based forming processes; as such, a robust and accurate method of optimal process parameter prediction is required. In this paper, goal driven optimization is conducted, utilizing numerical simulations as the basis for the prediction of optimal process parameters for the laser bending of DP 1000 steel. A key consideration of the optimization process is the requirement for minimal microstructural transformation in automotive grade high strength steels such as DP 1000

    Neutrophil count prediction for personalized drug dosing in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

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    Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer that usually affects children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction, intensification, and maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts which can lead to a condition known as neutropenia. This carries a risk of secondary infection and has been linked to 60% ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis and is based upon clinical judgment and experience of the medical professionals involved. Conceived as a decision support aid for clinicians then, presented are the results of a machine learning technique that predicts neutrophil counts one or more weeks ahead using data from ALL blood test results and 6-MP dosing. In this work, a model is trained and validated using data from a single female ALL patient’s maintenance phase. The prediction error is found to be typically within +/- 290/microL at one week and within +/- 820/microL for a 14 day prediction
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