Multicomponent kinetic determinations using artificial neural networks

Neural networks were successfully used for multicomponent kinetic determinations of species with rate constant ratios approaching unity without the aid of spectral discrimination. The ensuing method relies on two inputs describing the profile of the kinetic curve for each mixture, which is obtained by preprocessing kinetic data using nonlinear least-squares regression. A straightforward network architecture (2:4s:21) was used to resolve mixtures of 2- and 3-chlorophenol; the trained network estimated the concentrations of both components in the mixture with a relative standard error of prediction of approximately 5%, which is much lower than that obtained with Kalman filtering. The effect of some variables such as the rate constant and analyte concentration ratios on the proposed multicomponent determination is discussed.     

Simulation of complex movements using artificial neural networks

A simulated network for controlling a six-legged, insect-like walking system is proposed. The network contains internal recurrent connections, but important recurrent connections utilize the loop through the environment. This approach leads to a subnet for controlling the three joints of a leg during its swing which is arguably the simplest possible solution. The task for the stance subnet appears more difficult because the movements of a larger and varying number of joints (9-18: three for each leg in stance) have to be controlled such that each leg contributes efficiently to support and propulsion and legs do not work at cross purposes. Already inherently non-linear, this task is further complicated by four factors: 1) the combination of legs in stance varies continuously. 2) during curve walking, legs must move at different speeds, 3) on compliant substrates, the speed of the individual legs may vary unpredictably, and 4) the geometry of the system may vary through growth and injury or due to non-rigid suspension of the joints. This task appears to require some kind of "motor intelligence". We show that an extremely decentralized, simple controller, based on a combination of negative and positive feedback at the joint level, copes with all these problems by exploiting the physical properties of the system.     

A hybrid method to cluster protein sequences based on statistics and artificial neural networks

We have recently proposed a method, based on artificial neural networks (ANNs) to cluster protein sequences into families according to their degree of sequence similarity. The network was trained with an unsupervised learning algorithm, using, as inputs, matrix patterns derived from the bipeptide composition of the protein sequences. We describe here some further improvements to that approach. First, we propose a statistical method to cluster a set of bipeptidic matrices into families. It consists of three stages: (i) principal component analysis, (ii) determination of the optimal number M of clusters and (iii) final classification of the bipeptidic matrices into M clusters. Using a set of 444 protein sequences, we show that the classification given by the statistical method is in agreement with biological knowledge. We also show that the resulting classification is very similar to the one previously obtained with the ANN approach. Finally, we propose a new hybrid method of the statistical and ANN approaches, in which the results of the statistical method are used to choose the number of neurons and inputs of the network. We show that a network built in this way, and fed with a few principal components of the set of bipeptidic matrices as input signals, can be trained in an extremely short computing time. The resulting topological maps do not essentially differ from the ones obtained with the initial ANN approach.     

Comparison of hot rolled steel mechanical property prediction models using linear multiple regression,non-linear multiple regression and non-linear artificial neural networks

Abstract

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed. In the present paper a comparison of the application of linear multiple regression, non-linear multiple regression and non-linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non-linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non-linear multiple regression.     

Evaluation of complications of kidney transplantation using artificial neural networks

The aim of this study was to develop an artificial neural network (ANN) to differentiate between rejection, acute tubular necrosis (ATN) and normally functioning kidneys in a group of patients with renal transplants. The performance of ANN was compared with that of an experienced observer using a database of 35 patients' records, each of which included 12 quantitative parameters derived from renograms and clinical data as well as a clinical evaluation. These findings were encoded as features for a three-layered neural network to predict the outcome of biopsy or clinical diagnosis. The network was trained and tested using the jackknife method and its performance was then compared to that of a radiologist. The network was able to correctly classify 31 of the 35 original cases and gave a better diagnostic accuracy (88%) than the radiologist (83%), by showing an association between the quantitative data and the corresponding pathological results (r = 0.78, P < 0.001). We conclude that an ANN can be trained to differentiate rejection from acute tubular necrosis, as well as normally functioning transplants, with a reasonable degree of accuracy.     

A medical expert system approach using artificial neural networks for standardized treatment planning

PURPOSE: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. METHODS AND MATERIALS: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. RESULTS: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. CONCLUSIONS: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.     

Automated interpretation of myocardial SPECT perfusion images using artificial neural networks

The purpose of this study was to develop a computer-based method for automatic detection and localization of coronary artery disease (CAD) in myocardial bull's-eye scintigrams. METHODS: A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest-stress scintigraphy and coronary angiography within 3 mo was studied. Different image data reduction methods, including pixel averaging and two-dimensional Fourier transform, were applied to the bull's-eye scintigrams. After a quantitative and qualitative evaluation of these methods, 30 Fourier components were chosen as inputs to multilayer perceptron artificial neural networks. The networks were trained to detect CAD in two vascular territories, using coronary angiography as gold standard. A "leave one out" procedure was used for training and evaluation. The performance of the networks was compared to those of two human experts. RESULTS: One of the human experts detected CAD in one of two vascular territories, with a sensitivity of 54.4% at a specificity of 70.5%. The sensitivity of the networks was significantly higher at that level of specificity (77.2%, p = 0.0022). The other expert had a sensitivity of 63.2% at a specificity of 61.5%. The networks had a sensitivity of 77.2% (p = 0.038) at this specificity level as well. The differences in sensitivity between human experts and networks for the other vascular territory were all less than 6% and were not statistically significant. CONCLUSION: Artificial neural networks can detect CAD in myocardial bull's-eye scintigrams with such a high accuracy that the application of neural networks as clinical decision support tools appears to have significant potential.     

On the use of Bayesian methods for evaluating compartmental neural models

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience--compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.     

Applying artificial neural network models to clinical decision making.

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against future data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction of shape defects over length of cold rolled sheet using artificial neural networks

Abstract

A model based on an artificial neural network (ANN) has been developed for prediction of flatness of cold rolled (CR) sheet in a tandem cold rolling mill for white goods applications. Various process parameters including roll bending, roll shifting, tensions between stands etc., which affect flatness of CR sheet are considered in the model. Substantial amounts of data are obtained from level II automation of PL-TCM of TATA Steel to develop the prediction model. The developed ANN model, based on back propagation algorithm, is able to predict the flatness defects like edge buckles, centre buckles for a given set of rolling parameters. The model involves a large number of process parameters and application of ANN to such kind of problems is successfully demonstrated in the present study. The model is in good agreement with the observed flatness values at different locations across the width. High coefficient of determination close to 0·919 is achieved for the prediction of flatness at edges.     

Prediction of the measured temperature after the last finishing stand using artificial neural networks

In this report the development of an artificial neural network, capable of predicting the temperature after the last finishing stand of a hot strip mill for a certain class of steels, is described. Three neural networks with different numbers of hidden nodes (3, 5 and 7) were trained. The relative standard deviation in finish temperature as predicted by the best performing neural network model (7 hidden nodes) was just over 25% smaller than that of the linear Hoogovens model. This improved accuracy can be explained by the incorrect assumption in the Hoogovens model of linear dependence of the finishing temperature on some input parameters. With the trained neural network, the influence of the various input parameters on the finishing temperature could be examined. The dependencies predicted by the neural network can be approximated by a linear fit and are a factor 2 lower for all input parameters. It is conceivable that operation of the mill using an artificial neural network for the prediction of the finishing temperature would have resulted in smaller operational fluctuations.     

Artificial neural networks as models of stimulus control

We evaluate the ability of artificial neural network models (multilayer perceptrons) to predict stimulus-response relationships. A variety of empirical results are considered, such as generalization, peak shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the experiments considered. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. We conclude that these models are valuable tools in the study of animal behaviour. (c) 1998 The Association for the Study of Animal Behaviour.     

Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks

An artificial neural network (ANN) was developed to investigate whether hoof wall deformation could be used to determine ground reaction forces (GRF) in horses. The ANN was taught this relationship under certain conditions and was able to generalise this knowledge to conditions for which it was not trained before. To acquire data to train and test the ANN, a horse was equipped with strain gauges attached to the dorsal, lateral and medial parts of the hoof to assess hoof wall deformation. A force plate was used to measure the GRFs. Both hoof wall deformation and GRF were recorded simultaneously at different speeds, gaits, surfaces and loads. An ANN was trained with some of these data, and subsequently provided with strain gauge recordings of strides, not used for training. By comparing the GRF calculated by the ANN based on the hoof wall deformation with that recorded simultaneously by the force plate, the generalisability of the ANN was determined. It was found that an ANN is capable of 'learning' the relationship between hoof wall deformation and GRF, and to generalise it to a wide range of new conditions. This technique enables assessment of GRF under difficult conditions, such as on a treadmill or on surfaces where a force plate cannot be employed.     

A double bootstrap method to analyze linear models with autoregressive error terms.

A new method for the analysis of linear models that have autoregressive errors is proposed. The approach is not only relevant in the behavioral sciences for analyzing small-sample time-series intervention models, but it is also appropriate for a wide class of small-sample linear model problems in which there is interest in inferential statements regarding all regression parameters and autoregressive parameters in the model. The methodology includes a double application of bootstrap procedures. The lst application is used to obtain bias-adjusted estimates of the autoregressive parameters. The 2nd application is used to estimate the standard errors of the parameter estimates. Theoretical and Monte Carlo results are presented to demonstrate asymptotic and small-sample properties of the method; examples that illustrate advantages of the new approach over established time-series methods are described. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparative study of the stress flow models for high-boron corrosion-resistant steel based on an Arrhenius-type equation and artificial neural networks

A comparative analysis is performed for the stress flow models of a steel containing 2.3 wt % B during its compressive hot deformation based on an Arrhenius-type equation and artificial neural networks. It is shown that the accuracy of the obtained data using the model based on artificial neural networks is much higher (relative calculation error is 0.2%) as compared to that of the regression model (relative error is 11%).