A discriminant analysis of organizational performance variables.

Divided 98 groups participating in a gaming simulation (each group representing a company) into quartiles on the basis of their return on investment. A discriminant function analysis was utilized to test the following hypotheses: (a) that return on investment would divide the companies into the same quartiles as 12 other performance variables used in evaluating the groups and (b) that the most significant of the 12 performance variables identified by the discriminant function in Hypothesis 1 would remain constant over time. The 1st hypothesis was supported but the 2nd was not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction of Scour Downstream of Grade-Control Structures Using Neural Networks

A new approach for predicting local scour downstream of grade-control structures based on neural networks is presented. An explicit neural networks formulation (ENNF) is developed using a transfer function (sigmoid) and optimal weights obtained from a training process. A genetic algorithm was used to optimize the neural network architecture and the optimal weights for input and output parameters were obtained using the Levenberg–Marquardt back-propagation algorithm. Experimental data available in the literature, including large-scale results were used for training and validation of the proposed model. The predictive performance of the ENNF was found superior to other regression-based equations and the robustness of ENNF was evaluated using field data.     

Discriminating variables among psychotherapy dropouts from a psychological training clinic.

In contrast to most previous research on psychotherapy dropout, this study hypothesized that significant differences would be found among clients who terminate prematurely during the 3 major phases of therapy: intake, evaluation, and therapy proper. For 624 clients receiving individual, dynamically oriented therapy at a psychological training clinic, discriminant function analyses revealed that each of the 3 phases of therapy can be significantly distinguished with regard to the client characteristics related to dropout. Discriminating variables included demographics, psychiatric ratings, and diagnostic ratings. Compared with completers, intake dropouts were categorized with 77.06% accuracy (p?p?p?     

Efficacy of pancreatic transplantation on cardiovascular alterations in diabetic rats: an ultrastructural and immunohistochemical study

OBJECTIVES: To determine whether a neural network is superior to standard computational methods in predicting stone regrowth after shock wave lithotripsy (SWL) and to determine whether the presence of residual fragments, as an independent variable, increases risk. METHODS: We reviewed the records of 98 patients with renal or ureteral calculi treated by primary SWL at a single institution and followed up for at least 1 year; residual stone fragment growth or new stone occurrence was determined from abdominal radiographs. A neural network was programmed and trained to predict an increased stone volume over time utilizing input variables, including previous stone events, metabolic abnormality, directed medical therapy, infection, caliectasis, and residual fragments after SWL. Patient data were partitioned into a training set of 65 examples and a test set of 33. The neural network did not encounter the test set until training was complete. RESULTS: The average follow-up period was 3.5 years (range 1 to 10). Of 98 patients, 47 had residual stone fragments 3 months after SWL; of these 47, 8 had increased stone volume at last follow-up visit. Of 51 patients stone free after SWL, 4 had stone recurrence. Coexisting risk factors were incorporated into a neural computational model to determine which of the risk factors was individually predictive of stone growth. The classification accuracy of the neural model in the test set was 91%, with a sensitivity of 91%, a specificity of 92%, and a receiver operating characteristic curve area of 0.964, results significantly better than those yielded by linear and quadratic discriminant function analysis. CONCLUSIONS: A computational tool was developed to predict accurately the risk of future stone activity in patients treated by SWL. Use of the neural network demonstrates that none of the risk factors for stone growth, including the presence of residual fragments, is individually predictive of continuing stone formation.     

Pharmaceutical granulation and tablet formulation using neural networks

Current-day pharmaceutical formulation may be trial and error in nature due to the absence of a clear relationship between the formulation characteristics (output variables) and the material and process variables (input variables). Neural networks are networks of adaptable nodes, which through a process of learning from task examples, store experiential knowledge and make it available for prediction. Prediction of a model granulation and tablet system characteristics from the knowledge of material and process variables utilizing neural networks is the basis of this presentation. The formulation design contained the following variables: granulation equipment, diluent, method of binder addition, and the binder concentration. The material, process, granulation evaluation, and tablet evaluation data of the formulations were used as the data set for training and testing of the neural network models. A comparison of the neural network prediction performance with that of regression models was also done. Both the granulation model and the tablet model converged fairly rapidly in the training step. In the testing step, the predictions for all granulation model variables (geometric mean particle size, flow value, bulk density, and tap density) were satisfactory. In the tablet model, the predictions for disintegration and thickness were also satisfactory. The predictions for hardness and friability were less than satisfactory. Two situations where the neural network may not perform adequately are discussed. The neural network prediction is better or comparable for all the predicted variables in this study compared to regression methods. The results clearly show the applicability of neural networks to formulation modeling.     

Classification of observational data with artificial neural networks versus discriminant analysis in pharmacoepidemiological studies--can outcome of fluoxetine treatment be predicted?

For several years, there has been an ongoing discussion about appropriate methodological tools to be applied to observational data in pharmacoepidemiological studies. It is now suggested by our research group that artificial neural networks (ANN) might be advantageous in some cases for classification purposes when compared with discriminant analysis. This is due to their inherent capability to detect complex linear and nonlinear functions in multivariate data sets, the possibility of including data on different scales in the same model, as well as their relative resistance to "noisy" input. In this paper, a short introduction is given to the basics of neural networks and possible applications. For demonstration, a comparison between artificial neural networks and discriminant analysis was performed on a multivariate data set, consisting of observational data of 19738 patients treated with fluoxetine. It was tested, which of the two statistical tools outperforms the two other in regard to the therapeutic response prediction from the clinical input data. Essentially, it was found that neither discriminant analysis nor ANN are able to predict the clinical outcome on the basis of the employed clinical variables. Applying ANN, we were able to rule out the possibility of undetected suppressor effects to a greater extent than would have been possible by the exclusive application of discriminant analysis.     

BP神经网络在连铸板坯质量在线诊断中的应用

为了更好地应用BP神经网络对连铸板坯质量进行在线诊断,基于连铸生产特点,利用采集的过程数据建立了符合生产实际的均一化函数.通过分析BP神经网络中各参数对网络性能及诊断准确率的影响,对BP神经网络的结构及学习算法进行修正,使该网络有选择和有区分地学习铸坯质量知识.结合某钢厂连铸现场数据,以黏结为例,建立了6种网络模型,对各模型算法进行了比较测试.结果表明:采用自定义函数均一化样本或采用提出的差异性算法训练神经网络,均可明显提高诊断准确率;采用选择性算法可确保诊断准确率不变的同时,提高学习速度;修正的算法更能很好地符合连铸生产实际.     

Adverse effects of tribromoethanol as used in the production of transgenic mice

The authors developed and cross-validated prediction models for newly diagnosed cases of liver disorders by using logistic regression and neural networks. Computerized files of health care encounters from the Fallon Community Health Plan were used to identify 1,674 subjects who had had liver-related health services between July 1, 1992, and June 30, 1993. A total of 219 subjects were confirmed by review of medical records as incident cases. The 1,674 subjects were randomly and evenly divided into training and test sets. The training set was used to derive prediction algorithms based solely on the automated data; the test set was used for cross-validation. The area under the Receiver Operating Characteristic curve for a neural network model was significantly larger than that for logistic regression in the training set (p = 0.04). However, the performance was statistically equivalent in the test set (p = 0.45). Despite its superior performance in the training set, the generalizability of the neural network model is limited. Logistic regression may therefore be preferred over neural network on the basis of its established advantages. More generalizable modeling techniques for neural networks may be necessary before they are practical for medical research.     

Artificial Neural Network for Measuring Organizational Effectiveness

An artificial neural network based methodology is applied for predicting the level of organizational effectiveness in a construction firm. The methodology uses the competing value approach to identify 14 variables. These are conceptualized from four general categories of organizational characteristics relevant for examining effectiveness: structural context; person-oriented processes; strategic means and ends; and organizational flexibility, rules, and regulations. In this study, effectiveness is operationalized as the level of performance in construction projects accomplished by the firm in the past 10 years. Cross-sectional data has been collected from firms operating in institutional and commercial construction. A multilayer back-propagation neural network based on the statistical analysis of training data has been developed and trained. Findings show that by applying a combination of the statistical analysis and artificial neural network to a realistic data set, high prediction accuracy is possible.     

Variational learning in nonlinear gaussian belief networks

We view perceptual tasks such as vision and speech recognition as inference problems where the goal is to estimate the posterior distribution over latent variables (e.g., depth in stereo vision) given the sensory input. The recent flurry of research in independent component analysis exemplifies the importance of inferring the continuous-valued latent variables of input data. The latent variables found by this method are linearly related to the input, but perception requires nonlinear inferences such as classification and depth estimation. In this article, we present a unifying framework for stochastic neural networks with nonlinear latent variables. Nonlinear units are obtained by passing the outputs of linear gaussian units through various nonlinearities. We present a general variational method that maximizes a lower bound on the likelihood of a training set and give results on two visual feature extraction problems. We also show how the variational method can be used for pattern classification and compare the performance of these nonlinear networks with other methods on the problem of handwritten digit recognition.     

Genetic Programming to Predict Bridge Pier Scour

Bridge-pier scour is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by the writers), in the form of artificial neural networks (ANNs) and genetic programming (GP). There had been 398 data sets of field measurements that were collected from published literature and were used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in the training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth at bridge piers.     

���ڻ�ɫ����������ELM��¯�¶�Ԥ��ģ��

Aiming at the inherent defects of the traditional blast furnace temperature model, a kind of grey relational analysis based ELM (extreme learning machine) temperature prediction model was put forward. Due to the characteristics of ironmaking process with multivariable nonlinear, strong coupling, the traditional modeling methods were unable to meet the requirements of high precision prediction of blast furnace temperature. Firstly, the correlation of input variables was analyzed with the gray correlation analysis, and then the performance of the model was improved. Secondly, combined with analytical variables, the neural network was trained by ELM learning algorithm. Finally, the field data was used for training and testing of the network, and then compared with the traditional model. The results show that the model can predict the blast furnace temperature quickly and accurately, and also can meet the guide workers to manipulate the needs of blast furnace.     

Bayesian Regularization Neural Networks for Prediction of Austenite Formation Temperatures （A cl and A c3 ）

A neural network with a feed forward topology and Bayesian regularization training algorithm is used to predict the austenite formation temperatures （At1 and A13） by considering the percentage of alloying elements in chemical composition of steel. The data base used here involves a large variety of different steel types such as struc- tural steels, stainless steels, rail steels, spring steels, high temperature creep resisting steels and tool steels. Scatter diagrams and mean relative error （MRE） statistical criteria are used to compare the performance of developed neural network with the results of Andrew% empirical equations and a feed forward neural network with ＂gradient descent with momentum＂ training algorithm. The results showed that Bayesian regularization neural network has the best performance. Also, due to the satisfactory results of the developed neural network, it was used to investigate the effect of the chemical composition on Ac1 and At3 temperatures. Results are in accordance with materials science theories.     

Dynamic Analysis of Nonlinear Frames by Prandtl Neural Networks

A new type of activation function, based on the use of the Prandtl–Ishlinskii operator, has been developed and used in the feed forward neural networks in order to improve their capabilities in learning to identify and analyze nonlinear structures subject to dynamic loading. The genetic algorithm has been used in its training. The neural network, which is referred to as the Prandtl neural network here, has been trained and used in the analysis of two shear frames, a single degree of freedom (SDOF) and a 3DOF, both subjected to earthquake excitations. To assess the capabilities of the Prandtl neural network under ideal situations, the data on the response of the frames have been obtained through the integration of their governing nonlinear equations of motion. The training has been based on the white noise while the strong earthquakes of 200% El Centro in 1940 and Gilroy have been used for testing. Results have shown the high precision of the Prandtl neural network in solving highly hysteretic problems. The issue is important for two main applications in structural dynamics and control: (1) analysis of highly nonlinear structures where it is desired to train a neural network to directly learn the behavior of a structure from experimental data; and (2) intelligent active control of structures where neural network emulators are designed to provide as precise predictions about the future response of the structures as possible, in order to be used in the determination of the required control forces.     

Studies on the Transition Emission of Eu~（2＋） Ion in Complex Fluorides Using Neural Network

ＳｔｕｄｉｅｓｏｎｔｈｅＴｒａｎｓｉｔｉｏｎＥｍｉｓｓｉｏｎｏｆＥｕ~（２＋）ＩｏｎｉｎＣｏｍｐｌｅｘＦｌｕｏｒｉｄｅｓＵｓｉｎｇＮｅｕｒａｌＮｅｔｗｏｒｋ￥ＸｕＬｕ；ＹａｎｇＹｉ－Ｑｉｕ；ＨｕＣｈａｎｇ－Ｙｕ（ＡｐｐｌｉｅｄＳｐｅｃｔｒｏｓｃｏｐｙ...     

Variables that distinguish elementary teachers who participate in school-based consultation from those who do not.

Examined variables that distinguished elementary teachers who participated in consultation from those who did not. 352 female elementary teachers completed questionnaires: 186 of the Ss reported they had participated in consultation with a school psychologist, and 166 reported that they had not. A stepwise discriminant function analysis using 8 teacher response variables found 5 variables that significantly distinguished the 2 groups: school psychologist offering help, teachers' scores on the Problem Solving Inventory, perceptions of psychologist training in problem solving, years of teaching experience, and perceptions that a school psychologist's training is different from that of a teacher's. Suggestions are offered to school psychologists for increasing teacher requests for consultation services. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties

Artificial neural network (ANN) models are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application. A database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in the evaluation of the developed models. A commercial software, STATISTICA 7.1, is used to develop four different feedforward-type ANN models: linear network, general regression neural network, radial basis function network, and multilayer perceptrons network (MLPN). In each of these models, the input layer consists of seven nodes, one node for each of the independent variables, namely moisture content (w), dry density (γd), plasticity index (PI), percent passing sieve No. 200 (P200), unconfined compressive strength (Uc), deviatoric stress (σd), and bulk stress (θ). The output layer consists of only one node—resilient modulus (MR). After the architecture is set, the development data set is fed into the model for training. The strengths and weaknesses of the developed models are examined by comparing the predicted MR values with the experimental values with respect to the R2 values. Overall, the MLPN model with two hidden layers was found to be the best model for the present development and evaluation data sets. This model as well as the other models could be refined using an enriched database.     

Job characteristics and job attitudes: A multivariate study.

Obtained attitude data from 556 employees in a western telephone company. Respondents held 1 of 16 "craft" jobs in the department selected for study. Multiple discriminant function analysis was performed using 16 groups formed on the basis of Ss' job titles. Variables used in this primary analysis included job satisfaction, organizational commitment, motivational force, and sources of organizational attachment. Discriminatory power for the 16 group solution was .53. A secondary analysis was performed in which discriminant function means were related to means of jobs on several job characteristics variables. Viewed jointly these 2 analyses suggest that the relatively high discriminatory power achieved in the primary analysis may have been a function of job scope-job attitude relationship demonstrated in the secondary analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters

A neural network approach was employed to relate risky Cryptosporidium and Giardia concentrations with other biological, chemical and physical parameters in surface water. A set of drinking water samples was classified as “risky” and “nonrisky” based on the concentrations of full and empty oocysts, and cycsts of Cryptosporidium and Giardia, respectively. Given the constraints associated with collecting large sets of microbial data, the study was aimed at identifying an effective training algorithm that would maximize the performance of a neural network model working with a relatively small dataset. A number of algorithms for training neural networks, including gradient search with first- and second-order partial derivatives, and genetic search were used and compared. Results showed that genetic algorithm based neural network training consistently provided better results compared to other training methods.