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Aims: Lateral body weight shifting tasks are used in rehabilitation programmes, particularly in programmes for patients with orthopaedic complaints or hemiparesis. In the present study, we aimed to identify the inherent factors that could lead to deterioration of performance in such tasks. Methodology: Twenty four healthy participants were asked to accurately load a target of one or two thirds of their body weight onto their left and right lower limbs. Results: Constant error showed a relative central tendency effect during body weight shifting. ‘Central tendency’ refers to people undershooting heavier target loads by a large extent, and undershooting lighter target loads by a small extent during body weight shifting. Overall accuracy (root-mean-squared error) and variability significantly differed for the two target loads during weight shift to the left but not during that to the right. Inter-limb weight adjustment between the upper and lower limbs deteriorated the weight adjustment for the target load. Conclusion: Having a central tendency can potentially deteriorate a person’s performance of the task both in the early (when a lighter target is used) and late (when a heavier target is used) phases of rehabilitation. Lateral differences in both overall accuracy and consistency may occur while shifting heavy (rather than light) loads, particularly when the left lower limb is affected. Heavy reliance on the upper limb while attempting accurate loading on the target limb can also deteriorate loading accuracy.

Fractional regression hot deck imputation (FRHDI) imputes multiple values for each instance of a missing dependent variable. The imputed values are equal to the predicted value plus multiple random residuals. Fractional weights enable variance estimation and preserve correlations. In some circumstances with some starting weight values, existing procedures for computing FRHDI weights can produce negative values. We discuss procedures for constructing non-negative adjusted fractional weights for FRHDI and study performance of the algorithm using simulation. The algorithm can be used effectively with FRDHI procedures for handling missing data in the context of a complex sample survey.

This research compares the two features within an images, namely, color and edge characteristic and adjusting weight ratios from color weight value 1, edge weight value 1, and 3 color to edge weight ratios; 0.7 to 0.3, 0.5 to 0.5, and 0.3 to 0.7 to retrieve the most relevance query outputs. Based on the results, Color to edge weight ratio at 0.5 to 0.5 retrieved the most relevance query images outputs on RGB color models. The recall value and accuracy remained robust while F-measurements is fair. The HSV model evaluation was fair on both recall value and accuracy, but F-measure signified revision.

A money’s nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton’s method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E’). In our system, we use 5 types of money’s nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our re...

In order to solve the problem how to determine the quantitative relationship of characteristic factors caused by uncertainties, this paper describes the target case by characteristic factors in case-based reasoning, determines the initial weight according to the characteristic factor matrix, introduces Hebb learning rule to make a weight adjustment and applies the improved Euclidean distance to calculate the similarity between the cases to look for the most similar case. The similarity comparison between CBR based on Grey Correlation and the proposed method proves this model a better performance. At last, the application example of the prediction model after an earthquake can prove the feasibility.

In order to improve the efficiency of prediction and diagnose of crop diseases and pests, and solve the problems during the course of crop production and management, then an neural networks with weight adjustment of prediction model is proposed. The crop disasters are regarded as example, after the symptoms of disasters and features are classified, abstracted and coded, the adjustment of weight, optimization of network structure and reasonable adjustment of parameters of BP neural network are discussed, then a model is constructed to forecast the disasters of crop, the weight is used as knowledge to predict disasters of crop through studying training samples. Results have shown that the optimization expert system of crop disasters based on neural network has enhanced the ability of decision making of expert system, then greatly improved the accuracy and reliability of crop diagnosis.

The Web database's classification is the key step which integrates with the Web database classification and retrieves. The traditional search engine is unable to correct search for the magnanimous information in Deep Web hides. This article has proposed one kind of classification based on machine learning's web database. The experiment has indicated that after this taxonomic approach undergoes few sample training, it can achieve the very good classified effect, and along with training sample's increase, this classifier's performance maintains stable and the rate of accuracy and the recalling rate fluctuate in the very small scope.

The switched reluctance motor (SRM) is applied in various industrial applications due to its profitable advantages. However, the robustness speed of SRM is one of the major drawbacks, which greatly affects the performance of motor. Thus, the aim of this paper is to control the speed of SRM using H-infinity control strategy. This H-infinity control technique is stronger against robustness. In the proposed speed controller, the rotor position of the SRM is applied to the controller. The speed variation of the rotor is determined from the reference speed and applied to the controller as input. Then, the speed variation and the corresponding sensitivity function are determined. The sensitivity function determination is based on the input weight of the controller. The weight adjustment process is repeated until a stable speed condition is achieved. Then, the output of the proposed control technique is compared with the existing control technique and the robustness is analyzed. Here, the existing control techniques considered are proportionalintegral (PI) controller and fuzzy logic controller (FLC)-based PI gain tuning. The proposed control strategy is simulated in MATLAB working platform and the control performance is analyzed.

The predictive ability of Artificial Neural Network (ANN) on the effect of the concentration (30, 40, 50 y 60 % w/w) and temperature (30, 40 y 50°C) of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95). Se evaluó la capacidad predictiva de la Red Neuronal Artificial (RNA) en el efecto de la concentración (30, 40, 50 y 60 % p/p) y temperatura (30, 40 y 50°C) de la solución de fructooligosacaridos (FOS) en la masa, humedad, volumen y sólidos en cubos de yacón osmodeshidratados, y en el coeficiente de difusividad efectiva media del agua, con y sin encogimiento. Se aplicó la RNA del tipo Feedforward con los algoritmos de entrenamiento Backpropagation y de ajuste de pesos Levenberg-Marquardt, usando la topología: error meta de 10-5, tasa de aprendizaje de 0.01, coeficiente de momento de 0.5, 2 neuronas de entrada, 6 neuronas de salida, una capa oculta con 18 neuronas, 15 etapas de entrenamiento y funciones de transferencia logsigpurelin. El error promedio global por la RNA fue 3.44% y los coeficientes de correlación fueron mayores a 0.9. No se encontraron diferencias significativas entre los valores experimentales con los valores predichos por la RNA y con los valores predichos por un modelo estadístico de regresión polinomial de segundo orden (p > 0.95).

Ophthalmic sponges are used to collect undiluted cervical secretions for assessment of markers of genital tract immunity. Heterogeneity in absorbed and extracted sample volumes requires normalization in order to make valid inter-individual comparisons. We evaluated the performance of adjustment by weight and total protein on normalizing inter-individual variability of immune marker measurement due to differences in volume collection. Normalization to total protein resulted in a minimal loss of usable specimens and a significant reduction in the correlation of immune marker concentration to specimen weight compared to weight adjustment. Total protein normalization appeared to be more effective than weight adjustment in reducing the dependence of cervical immune marker concentrations on differences in specimen volume.

Recently, although the need of marine robots being raised in extreme areas, the basis is very deficient. Fortunately, as the robot competition is vitalizing and the need of the robot education is increasing, it is desirable to establish the basis of the R&D and industrialization of marine robots and to train professionals through the development and diffusion of marine robot kits. However, in conventional case, there is no underwater-type autonomous marine robot kit for the marine robot competition, which has the abilities of the underwater locomotion and target detection and avoidance. To solve this problem, a marine robot kit which has the abilities of the underwater locomotion, the waterproof and the weight adjustment, is developed. To verify the performance of the developed kit, test and evaluation such as surge, pitch, yaw, obstacle avoidance is performed. The test and evaluation results show that the possibility of the real applications of the developed kit.

A novel control scheme based on an improved RBF neural network and PID control method is proposed. When used the RBF network in the PID controller, if RBF network provides the parameter to revise PID while it does not have training finished, the controller will oscillated or even to diverge. As the structure is more complex, and the adjustment speed is slower, the improvements are made to the standard RBF neural network trained algorithm. Uses the matrix operation substitute the iterative algorithm, may avoid the above question effectively. Finally, choosing a certain type of PID controller, the improved RBF neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved RBF neural network algorithm can avoid oscillated and diverge.

Aiming at the problem that traditional PID control algorithm is difficult to get ideal control effect, a PID control algorithm based on improved BP neural network is proposed to improve the performance of turntable system. According to the structure and characteristic of BP neural network, the construction of PID controller and the description of improved BP neural network algorithm are introduced at first. Then, on the basis of the least square method and neural network prediction model of controlled object, the weight adjustment algorithm of PID is improved by replacing the measured values of BP network with calculated forecast output. A mathematical model of turntable control system is established and simulated. Simulation results show that the improved BP neural network PID controller has good control performance, high tracking accuracy and strong system robustness, which can be better applied to turntable system.

BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.

an Artificial Neural Network classifier is a nonparametric classifier. It does not need any priori knowledge regarding the statistical distribution of the class in a giver selected data Source. While, neural network can be trained to distinguish the criteria used to classify easily in a generalized manner that allows successful classification the newly arrived inputs not used during training. Through this paper it is eastliblished that back propagation neural network works successfully for the purpose of classification. Back propagation suffers from getting stuck into Local Minima. Weight optimization in Back propagation can be optimized using the Genetic Algorithm (GA). The back propagation algorithm is improved by invoking Genetic algorithm, to improve the overall performance of the classifier. The performance of a fitness algorithm using the approach suggested by us is a Hybrid System that is being analyzed in this paper. In this paper the issue of improving the fitness (weight adjustment) of Back propagation algorithm is addressed. Some of the Advantages of Hybrid algorithms are: convergence speed will be increased and the local minima problem can be overcome. The proposed Hybrid Algorithm is to perform learning as a back propagation and optimize weights using GA for classification.

The aim of the experiment was to study the behaviour of fast growing (FG) and slow growing (SG) broilers under the same weight load. For this purpose a harness and a suspension device were developed which alleviate a part of the weight load on the legs. The weight load in the SG birds was increased by fitting pieces of lead on the back and the breast. Using these methods the weight load of both lines was adjusted to the average weight of both lines. A total of 24 male broilers, 12 ISA S257 and 12 ROSS 308, were assigned to 4 groups of three birds each. One bird was weight adjusted, one was wearing the harness without weight adjustment and one was without harness. Duration and frequency of walking and resting behaviour were recorded. The torsional and angular deformity of tibiae were measured according to Leterrier (1992). The amount and thickness of the corticalis, bone density and area were measured at the middle tibia by computer tomography. The weight-reduced FG broilers showed higher activity in the 6th week of age. The weight-increased SG broilers showed less activity than the SG broilers without harness. The weight-reduced FG broilers showed similar activity as the SG broilers without harness. Total density and corticalis area were higher for the weight-reduced FG broilers than for the FG broilers without harness. The results show that weight load is the main influencing factor for differences in locomotor activity and resting behaviour of SG and FG broiler strains.

The integration of modern computing and advanced communication with power grids has led to the emergence of electrical cyber-physical systems (ECPSs). However, the massive application of communication technologies makes the power grids become more vulnerable to cyber attacks. In this paper, we study the vulnerability of ECPSs and develop defence strategies against cyber attacks. Detection and protection algorithms are proposed to deal with the emergency of cascading failures. Moreover, we propose a weight adjustment strategy to solve the unbalanced power flows problem which is caused by splitting incidents. A MATLAB-based platform with advantages of easy programming, fast calculation, and no damage to systems is built for the offline simulation and analysis of the vulnerability of ECPSs. We also propose a five-aspect method of vulnerability assessment which includes the robustness, economic costs, degree of damage, vulnerable equipment, and trip point. The study is of significance to decision makers as they can get specific advice and defence strategies about a special power system.

過剰な体重調整,とくに長期的な減量が身長発育や比体表面積( : s=体重(W : g)/体表面積(S : cm^2))に大きく影響するものと考えられることから,身長発育および比体表面積を考慮した体重調整の方法を明らかにするためにボクシングの選手を目指す高校生男子の縦断的な身長発育と比体表面積について検討することを目的とした。その結果,次のことが明らかにされた。1.競技能力のレベルが高い選手の方が競技能力の比較的低い選手よりも,1/sは基準値の3% ile.以上のものが多く認められた。2.縦断的にみた場合,身長発育の現量値は基準値の正常範囲にあり,競技能力のレベルが高い選手は低い選手よりも,1/sは正常範囲にあるものが多く認められた。3.身長発育に相応した体重調整をすることが,ボクシング選手の健康を維持し,しかもパフォーマンスを大きくするものと考察した。, This practical research case study is to develop a method for weight adjustment for long-term weight loss that takes into account the growth of height and specific body surface area ( : s = body surface area / weight) of male high school students training to be boxers. Thirty-two boxers participated in the inter-high-school athletic competition ( : IHSAC) and thirty-two boxers did not participate in the IHSAC. As a result, the following was revealed. 1. Most boxers who participated in the IHSAC, were higher than the 3 percentile of standard value for 1/s, compared to boxers who did not participate in the IHSAC. 2. The height growth of boxers who participated in the IHSAC and boxers who did not participate in the IHSAC fell within the normal range of the growth standard value. Many boxers who participated in the IHSAC were located in the bottom of the normal range for 1/s versus the boxers who did not participate in the IHSAC. 3. It can be concluded that the weight adjustment based on the growth of height and the specific body surface area increased maintenance of health and performances of boxers.

Under recent dramatic declines in response rates, various procedures have been considered among surveypractitioners to reduce nonresponse in order to avoid its potential impairment to the inference. In the randomdigit dialing telephone surveys, substantial e orts are often required to obtain the initial contact for the screenerinterview. To reduce a burden with higher data collection costs, refusal conversion can be administered only to arandom portion of the sample, reducing nonresponse (bias) with an expense of sample variability increment dueto the associated weight adjustment. In this paper, we provide ways to determine the optimal subsampling rateusing a linear cost model. Our approach for refusal subsampling is to predetermine a random portion from thefull sample and to apply refusal conversion e orts if needed only to the subsample.Keywords: Screener interview, cost model, design effect, weight adjustment. 1. Introduction Under recent dramatic declines in response rates (

Fighting sports are generally divided into categories defined by weight bands, and this is a constant concern for athletes, whomany times manipulate their body by using unsafe strategies mainly to reduce body weight. The aim of this work was to evaluate the impactof nutritional intervention in weight adjustment for each category of Olympic Box. The sample was composed by 10 athletes from the BrazilianBoxing Team in the period from January 2007 to February 2008. The strategies used for weight reduction and the anthropometric profile wereinvestigated. Each athlete received a customized alimentary planning. All athletes answered that they used strategies for altering their weight.The main ones were: the use of clothes and gadgets that increase sweat production, liquid ingestion restrictions, fast and semi-fast. At thebeginning, athletes presented an average 5,8kg above the weight of the category they were in. After three months of nutritional interventionthis average fell to 4,4kg and in 6 months to 2,8kg. The average reduction of fat percentage was 6,3%, showing a positive impact of the usednutritional intervention. Due to the damages produced by fast weight loss procedures on health and performance, it is extremely important toknow the anthropometric profile of athletes, preventing them to fight in categories not compatible anthropometric characteristics.

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.

Grey self-organizing map (GSOM) model is proposed and applied in the detection of intrusion. Through the improvement of the weight adjustment using the GRC (grey relational coefficient), the training results of SOM get better. In the detection of deny of service (DOS) attacks, this model can consider the relativity of the data set of DOS attacks. Finally, the experiments on the DOS data set confirm their validities and feasibilities over this GSOM model.

BLS adjustment methods have been able to provide more accurate estimates of total and make samples represent population characteristics by post-adjustment of design weights of samples. However, BLS methods use additional data, for instance number of employee, without this information or using other information, give different weight adjustment factors. In this paper we studied the sensitivity of the variables used in BLS adjustment. The 2007 monthly labor survey data is used in analysis.

This note comments on a paper by Triantaphyllou (J. Multi-Crit. Decis. Anal. 2001; 10: 11–25) that attempts to demonstrate new types of rank reversal that can occur with the analytic hierarchy process (AHP). He contends that the reversals are attributable to the various types of normalization that are used with the addition step in AHP synthesis. His paper goes on to suggest that the multiplicative AHP should be used instead. This note shows that the cause of the problem is another one: AHP’s independence axiom, which prohibits adjusting the criteria weights when the set of alternatives or the type of normalization change. If the criteria weights are adjusted properly, none of the rank reversals will occur

A non-discriminating criterion is defined as a criterion where the decision-maker is indifferent among the alternatives. One would therefore expect the final rank order of the alternatives not to be affected by removing it. A previously published paper by Finan and Hurley (Comput. Oper. Res. 2002; 29: 1025–1030) showed that in the analytic hierarchy process removing such a criterion from a multilevel hierarchy can reverse rank. In this paper, we offer an explanation of this particular rank reversal phenomenon and show how it can be avoided. We do this by taking into account that there is a link between the normalization and weighting processes, which suggests adjusting appropriate weights when removing criteria. Further, we discuss whether a non-discriminating criterion should be removed in the first place

In sample design, it is usually planned by purpose and the range of the announcing statistics from the survey. After survey, getting a proper and decent statistics, applying the proper weights on the results of survey is very important and necessary. Therefore in this study, three estimation methods which are raking, BLS and general linear regression method are compared with MSE, Coverage, CV, LE and NC.

The augmented-neural-network (AugNN) approach has been applied lately to some NP-Hard combinatorial problems, such as task scheduling, open-shop scheduling and resource-constraint project scheduling. In this approach the problem of search in the solution-space is transformed to a search in a weight-matrix space, much like in a neural-network approach. Some weight adjustment strategies are then used to converge to a good set of weights for a locally optimal solution. While empirical results have demonstrated the effectiveness of the AugNN approach vis-a-vis a few other metaheuristics, little theoretical insights exist which justify this approach and explain the effectiveness thereof. This paper provides some theoretical insights and justification for the AugNN approach through some basic theorems and also describes the algorithm and the formulation with the help of examples.

A polymorphic circuit can perform two or more functions under different conditions without the need of extra components. Those functions can be activated by environmental signals, such as temperature, power supply voltage, illumination, and so on. So far, existent evolutionary algorithms can only evolve polymorphic circuits of small scales. In this paper, the periodical weight adjustment method is proposed for the Evolutionary Strategy to evolve polymorphic circuits. The experimental results demonstrate that the Evolutionary Strategy with the periodical weight adjustment can perform better than existent evolutionary techniques for polymorphic circuits is evolved for the first time.

We propose a stimulus-stimulus association learning by coupling firing rate and precise spike timing encoding for spatio-temporal neural networks. We simulate a generic recurrent network with random and sparse connectivity consisting of Izhikevich spiking neurons. The magnitude of weight adjustment in learning is dependent on pre- and postsynaptic spikes based on their spikes count and time correlation. As a result of learning, synchronisation of activity among inter- and intra-subpopulation neurons demonstrates association between two stimuli. The associations show in spill-over of activity between the two stimuli involved.

In sample design, determining the weights of estimates becomes usually great influence on the result. In this article, raking methods are applied to different domain and depending on the range of the domain and sample size, the results of estimates are explained and compared. For the comparison, we use the MSE, MAE, MSPE and MAPE with Actual State of Minor Enterprisers Human Resources Survey data in 2001. The simulation result shows that more elaborate method is superior to the widely used method as expected but the difference is not quite significant.

Learning the preferences of users is an important problem in e-commerce research. This paper presents a system for that purpose, and it is primarily based on weight vectors. Learning is incorporated in the form of refining the weights. The system is sensitive to the users' change of trend and is implemented for labor profile domain. An empirical evaluation has been conducted in a simulated environment. The results proved the following: (1) The system converges if the user populations have some common preferences, (2) The system detects and adapts to a change of trend, and (3) It takes more time to converge in the case of more weights.

Large scale surveys in education have to face non-response issues that might bias the results. Non-response can occur at three levels: (i) a school refuses to participate, (ii) a sample student fails to participate and (iii) a participating student refuses to answer a particular question. Until now schools and student non-response have been counterbalanced by a non-response weight adjustment. This assumes that both the school and student non-respondents have similar characteristics to the school and student respondents, respectively, within classes. In this article results of analyses conducted on the Student Tracking Form data of the OECD/PISA 2000 survey are presented. The non-randomness of student absenteeism or refusal is demonstrated. Then, a simulation that compares the relative efficiency of the student weight adjustment with a multiple imputation method is presented where the superiority of the multiple imputation method, in particular for educational systems with a small school variance is shown. Finally, the multiple imputation method applied to some PISA 2000 countries identifies biases that become substantial in a longitudinal perspective.

Objective To assess whether existing weight correction formulas for PAPP-A and free-beta-hCG developed for weeks 11 to 14 can be applied to pregnancies in weeks 8 to 10. Methods Development of formulas based on limited data sets of 8- to 10-week pregnancies and comparison with existing formulas. Calculation of median MoMs adjusted with different formulas for weight correction. Results Weight correction formulas for the gestational age of 11 to 14 weeks were not appropriate in the 8- to 10-week gestational age interval for PAPP-A, whereas existing weight correction formulas could be applied to free-beta-hCG, independent of gestational age interval. Conclusion If PAPP-A is used in different gestational age intervals, weight corrections should be developed for the interval. Copyright © 2005 John Wiley & Sons, Ltd.

The load deployment on optical networks affects the connections recovery and new services. This paper proposes a risk-elimination and weight-adjustment algorithm. This algorithm eliminates risk-shared links from optical-networks links and adjusts link weights for the SPF algorithm. The two experiments show that the weight adjustment algorithm balances the load of networks and the risk-elimination algorithm improves the connections recovery.

Contacting with ground in the post-card area size only, tire supports entire automobile weight. As well, it characterizes most of automobile running performance. Among the design parameters, the carcass contour becomes a key design factor. This paper deals with the time-effective optimal design of tire crown contour in order to improve the tire wear performance by employing a back-propagation neural network model.

A meta-analytic study was conducted to investigate the causal relationships among individual, organizational and environmental factors related to nurses' intention to stay at or leave their jobs in Taiwanese hospitals. A total of 129 studies related to nursing turnover from 1978–1998 were reviewed. A total of 4032 subjects were selected for the study. Data were integrated by estimation of parametric correlation coefficients, and analyzed using Friedman's two-way analysis of variance by ranks following weight adjustment of sample size and estimation of correlation effect on size of variables. The results of this study merit attention by nursing administrators in order to develop strategies for stabilizing the nursing work force.

For the standard Back-Propagation training algorithm, the objective function is defined as the squared error between the actual and desired output. In [3], we introduced a modular system for training BP-networks by means of the so-called Generalized Relief Error Networks (GREN-networks) approximating the unknown objective function. In comparison with the standard Back-Propagation training algorithm, the proposed system does not require the knowledge of the desired output activity for the proper weight adjustment. During training, the necessary error terms are determined by the pre-trained GREN-network. Furthermore, GREN-networks can be applied for searching patterns "similar" to those presented to and recalled by the trained BP-network, but with a lower error at the output of the GREN-network. This searching process is performed iteratively by the applied GREN-network. Here, we will focus on analyzing the performance of the above model, especially with regard to its knowledge extraction ability. Experimen...

Weighted coefficients of image retrieval algorithm based on relevance feedback are determined in advance, which is lack of flexibility. In order to obtain satisfactory retrieval results, this algorithm requires a large amount of feedback calculation and efficiency of the algorithm is low. Aiming at the faults of relevance feedback, the adaptive adjustment algorithm of weighted coefficients based on quantum particle swarm optimization is presented, which is composed of user feedback process and particle evolution process. The particle encoding process and fitness function calculation process are worked out. The result of experiment using the Corel standard library, shows that quantum particle swarm optimization algorithm greatly improves the retrieval accuracy than the other image retrieval algorithms.

A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.

Kernel-based methods for NLP tasks have been shown to enable robust and effective learning, although their inherent complexity is manifest also in Online Learning OL scenarios, where time and memory usage grows along with the arrival of new examples. A state-of-the-art budgeted OL algorithm is here extended to efficiently integrate complex kernels by constraining the overall complexity. Principles of Fairness and Weight Adjustment are applied to mitigate imbalance in data and improve the model stability. Results in Sentiment Analysis in Twitter and Question Classification show that performances very close to the state-of-the-art achieved by batch algorithms can be obtained.

A new BP algorithm optimized by ant colony algorithm was proposed.Training error and test error of BP network were used to update and choose the information,in order to calculate the transfer probability in ants' route.Parameters value of sites in ants' route was bestowed on BP network,while parameters and training error stored in stored units were changed along with the adjustment of training error of BP network.Through iterative and mutual optimization,the best-optimized parameters of BP network were obtained.BP network based on ant colony algorithm was validated and tested for optimizing several functions.The results show that the training number of optimized BP network is smaller,and the precision of model is higher compared with the traditional BP algorithm while the training errors are of the same order of magnitude.

A subjective assessment of fork-lift truck seats has been carried out to assess the range of preferred seat dimensions and the acceptability of different seat adjustments to fork-lift truck drivers. Twelve fork-lift truck seats fitted to a fixed bench were assessed by twelve fork-lift truck drivers. For each seat, each driver completed a questionnaire that covered the following areas: eleven seat dimensions, four seat adjustments and other features (arm rests, safety belts and safety wings). The drivers assessed the dimensions of a seat chosen at random, before moving to the adjacent seat in either a clockwise or anticlockwise direction. Before each assessment, they were asked to look forward and backward in the seat as though they were driving a fork-lift truck. In general, significant correlations were obtained between the subjective assessments and objective measurements of the seat dimensions. This enabled preferred seat dimension ranges to be defined. Fork-lift truck drivers ranked the forward-backward and the backrest inclination adjustments as most important. The results showed that although the drivers had previous experience in the use of suspension seats, they did not understand the purpose of the weight adjustment. All adjustments should be easy to find, accessible, easy to move and they should enable a range of adjustments. In addition, clear and simple information on the seat is needed, especially for the weight adjustment. The most recently designed seats generally had dimensions within the preferred ranges compared with the older generation seats, mainly because they had more adjustments. However, the results show that adjustments on fork-lift truck seats could be improved further. Some tentative conclusions are made for the preferred fork-lift truck seat dimensions and adjustment ranges which are based on the evidence from this restricted sample of fork-lift truck drivers. They may be useful for seat and truck designers in the preparation of a standard on fork-lift truck seat dimensions.

Serum markers used in screening for Down syndrome and neural tube defects are often adjusted to take account of the effect of maternal weight on the marker levels. The standard adjustment procedure is based on a linear relationship between the marker concentration, expressed as the log of the multiple of the median (MOM), and maternal weight on a linear scale. It has been proposed that maternal weight adjustment may be better performed using a linear relationship between marker concentration expressed in MOM and the reciprocal of maternal weight. In a dataset of 8905 singleton pregnancies in white women without Down syndrome or neural tube defects we compared the two methods of weight adjustment and found that both were satisfactory and neither had an obvious advantage over the other. In the analysis it was noticed that hCG levels in very heavy women (> 120 kg) were higher than expected from the decreasing linear trend with maternal weight--a result that was statistically highly significant (p < 0.01) but for which we have no explanation. In screening it will have virtually no effect because the finding was restricted to only the 0.3 per cent of the heaviest women.

Scheduling is crucial to the operation of logistics service supply chain (LSSC), so scientific performance evaluation method is required to evaluate the scheduling performance. Different from general project performance evaluation, scheduling activities are usually continuous and multiperiod. Therefore, the weight of scheduling performance evaluation index is not unchanged, but dynamically varied. In this paper, the factors that influence the scheduling performance are analyzed in three levels which are strategic environment, operating process, and scheduling results. Based on these three levels, the scheduling performance evaluation index system of LSSC is established. In all, a new performance evaluation method proposed based on dynamic index weight will have three innovation points. Firstly, a multiphase dynamic interaction method is introduced to improve the quality of quantification. Secondly, due to the large quantity of second-level indexes and the requirements of dynamic weight adjustment, the maximum attribute deviation method is introduced to determine weight of second-level indexes, which can remove the uncertainty of subjective factors. Thirdly, an adjustment coefficient method based on set-valued statistics is introduced to determine the first-level indexes weight. In the end, an application example from a logistics company in China is given to illustrate the effectiveness of the proposed method.

This paper proposed an adaptive weight congestion control model in a vehicle transport network. Our focus was to construct a quantitative index series to describe the network congestion distribution, and to shunt vehicles on seriously congested links based on such index sequence. We achieved this goal by combing both feedback and iteration strategy in the congestion control field. First, we developed an agent based model which captured the nonlinear feedback mechanism between the vehicle routing behavior and the road congestion state. Then, the model implemented an adaptive intersection weight adjustment mechanism based on the evolutionary congestion degree of the nearby links, through which to achieve congestion distribution evaluation and network congestion control at the same time. The simulation results verified the validity of our model for congestion control under predefined networks, and proved an applicability of the intersection weight sequence as a measurement for the congestion degree and distribution of road networks.

Forty-two healthy women volunteered to have blood samples drawn at 2, 4, 8, 24, 48, and 168 h (7 days) following placement of levonorgestrel-releasing rod (LNG rod) or of Norplant capsule implants to permit measurement of drug concentrations. Three clinics recruited 10 women each, half of whom used each type of implant. Twelve additional subjects were later enrolled at one site to provide greater detail for the capsule implants. Throughout the week, women with Norplant implants had apparently higher mean drug concentrations than did women with LNG rod implants, but the differences were significant only in the first 48 h after placement (p0.05). Maximum levels for capsule implants were found at the 24-h sample (mean, 1358 to 1474 pg/mL) and for the LNG rod implants at 48 h (772 pg/mL). Body weight was negatively correlated with levonorgestrel concentrations at all times (p0.05). For several sampling times, differences between clinics in mean concentrations were statistically significant after weight adjustment. Despite differences by implant type, weight, or clinic location, drug concentrations compatible with contraceptive effect were attained within 24 h in users of LNG rod or of Norplant implants, insofar as all women had levonorgestrel concentrations above 250 pg/mL at the 24-h sample.To establish the minimum times required for contraceptive implants to achieve serum concentrations compatible with contraceptive action, drug concentrations were measured in blood samples collected from 42 women at 2, 4, 8, 24, and 168 hours after implant insertion. 15 volunteers from three centers (US, Chile, and the Dominican Republic) were fitted with a levonorgestrel (LNG)-releasing rod and 27 received Norplant implants. An LNG concentration of 250 pg/ml and above was considered indicative of contraceptive action. By 8 hours, all Norplant subjects but only 40% of the LNG rod subjects had achieved this serum level. By 24 hours, however, all women in both groups had LNG concentrations above 250 pg/ml. The maximum mean LNG level was recorded in the 24-hour sample (1358 pg/ml) in the Norplant group and at 48 hours (772 pg/ml) in the LNG rod group. Consistent with earlier investigations, LNG concentrations demonstrated strong and significant negative correlations with body weight at all time points. The paucity of pregnancies recorded in large-scale clinical trials in the first month of use of contraceptive implants supports the rapid attainment of effective LNG concentrations. It remains unclear, however, precisely how late in the menstrual cycle implants can be inserted and still prevent follicular development or halt ovulation. Until more information is available, continuation of the policy of implant placement in the first few days after the onset of menstruation is recommended.