Different Number Of Input Variables Research Articles

Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits.

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams

The aim of this study is to develop a novel computer-aided method for the prediction of the deflection of reinforced concrete beams (DRCB) under concentrated loads. To this end, in the present work, a Levenberg–Marquardt-based backpropagation novel neural network model, optimized by the whale optimization algorithm (WOA), called WOA-LMBPNN, has been developed. Specifically, a neural network, using the Levenberg–Marquardt backpropagation training algorithm with multiple hidden layers, was optimized by the WOA, aiming to obtain higher accuracy in predicting DRCB. For the training of the models, 120 experiments with the geometrical and mechanical properties of concrete beams were compiled using were used as the input parameters. Seven datasets with different number of input variables were investigated to evaluate the effect of the input variables on DRCB. For comparison purposes, another swarm optimization algorithm (i.e., particle swarm optimization—PSO) was also used to optimize the LMBPNN model (i.e., PSO-LMBPNN model). The results obtained by the PSO-LMBPNN and WOA-LMBPNN models are then compared based on the different datasets. Finally, the results revealed the effective role of the WOA, as well as the efficiency and robustness of the new hybrid WOA-LMBPNN model in predicting DRCB.

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.

Short-term Power Prediction of Wind Farm Power Based on BP Neural Network

With the proportion of wind power in the power systems increasing, consideration must be given to the fact that the randomness and volatility of wind power output will inevitably affect the stable operation of power grid. One of the effective ways to solve this problem is to forecast the output of wind power. In this paper, we employ the method of BP neural network to predict the wind power output in a period of time. To discuss the predictive performance of BP neural networks, we set different number of input variables to observe the prediction effect of BP neural network. We find that it’s not that the more input information, the better the prediction effect. The data with strong correlation can be used as input to achieve better results.

A neural-based predictive model of the compressive strength of waste LCD glass concrete

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

Identifying a Probabilistic Boolean Threshold Network From Samples.

This paper studies the problem of exactly identifying the structure of a probabilistic Boolean network (PBN) from a given set of samples, where PBNs are probabilistic extensions of Boolean networks. Cheng et al. studied the problem while focusing on PBNs consisting of pairs of AND/OR functions. This paper considers PBNs consisting of Boolean threshold functions while focusing on those threshold functions that have unit coefficients. The treatment of Boolean threshold functions, and triplets and -tuplets of such functions, necessitates a deepening of the theoretical analyses. It is shown that wide classes of PBNs with such threshold functions can be exactly identified from samples under reasonable constraints, which include: 1) PBNs in which any number of threshold functions can be assigned provided that all have the same number of input variables and 2) PBNs consisting of pairs of threshold functions with different numbers of input variables. It is also shown that the problem of deciding the equivalence of two Boolean threshold functions is solvable in pseudopolynomial time but remains co-NP complete.

Using ANN to Forecast Transportation Sector’s Energy Consumption in Taiwan Based on Oil and Gas Price

In this study, four types of artificial neural network (ANN) were adopted to forecast transportation sector’s energy consumption (TSEC) taking different number of input variables. By taking premium gasoline price (PGP), premium diesel oil price (PDOP), fuel oil price (FOP), raw material natural gas price (RMNGP), and fuel natural gas price (FNGP) as input variables, ANN could successfully forecast TSEC, the best mean absolute percentage error, mean square error, root mean square error, and correlation coefficient for training and testing were 15.03 % versus 24.43 %, 2792036.59 versus 11982081.08, 1670.94 versus 3461.51, and 0.71 versus 0.51, respectively.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.

Analysis of the influence of forestry environments on the accuracy of GPS measurements by means of recurrent neural networks

The present paper analyses the accuracy of the measurements performed by a global positioning system (GPS) receiver located in forested environments. A large set of observations were taken with a GPS receiver at intervals of one second during a total time of an hour at twelve different points placed in forest areas. Each of these areas was characterized by a set of forest stand variables (tree density, volume of wood, Hart-Becking index, etc.) The influence on the accuracy of the measurements of other variables related to the GPS signal, such as the position dilution of precision (PDOP), the signal-to-noise ratio and the number of satellites, was also studied.Recurrent neural networks (RNNs) were applied to build a mathematical model that associates the observation errors and the GPS signal and forest stand variables. A recurrent neural network is a type of neural network whose topology allows it to exhibit dynamic temporal behaviour. This property, and its utility for discovering patterns in non-linear and chaotic systems, make the RNN a suitable tool for the study of our problem.Two kinds of models with different numbers of input variables were built. The results obtained are in line with those achieved by the authors in previous research using different techniques; they showed that the variables with the highest influence on the accuracy of the GPS measurements are those related to the forest canopy, that is, the forest variables. The performance of the models of the RNN improved on previous results obtained with other techniques.

Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed.

Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal

Takagi-Sugeno-Kang fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms. Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Moreover two model variants with three and five triangular and trapezoidal membership functions describing each input variable were tested. Several TSK fuzzy models comprising different number of input variables were evaluated using the MATLAB. The evolutionary algorithms were based on Pittsburgh approach with the real coded chromosomes of constant length comprising whole rule base or both the rule base and all parameters of all membership functions. The experiments were conducted using training and testing sets prepared on the basis of actual 150 sales transactions made in one of Polish cities and located in a residential section. The results obtained were not decisive and further research in this area is needed.

Artificial neural networks in prediction of bone density among post-menopausal women

Artificial neural networks (ANN) are promising tools in learning complex interplay of factors on a particular outcome. We performed this study to compare the predictive power of ANN and conventional methods in prediction of bone mineral density (BMD) in Iranian post-menopausal women. A database of 10 input variables from 2158 participants was randomly divided into training (1400), validation (150) and test (608) groups. Multivariate linear regression and ANN models were developed and validated on the training, and validation sets and outcomes (femoral neck and lumbar T-scores) were predicted and compared on the test group using different numbers of input variables. Results were evaluated by comparing the mean square of differences between predicted and reference values (non-central chi-square test) and by measuring area under the receiver operating characteristic curve (AUROC) around cut-off value of -2.5 for T-scores. For models with less than 3 input variables in femoral neck and 4 variables in spinal column, performance of regression and ANN models was almost the same. As more variables imported into models, ANN outperformed linear regression models. AUROC varied in 2 to 10 variable models as follows: for ANN in spine, from 0.709 to 0.774; linear models in spine, from 0.709 to 0.744; ANN in femoral neck, from 0.801 to 0.867; linear models in femoral neck, from 0.799 to 0.834. The ANN model performed better than five established patient selection tools in the test group. Superior performance of neural networks than linear models demonstrate their advantage especially in mass screening applications, when even a slight enhancement in performance results in significant decrease in number of misclassifications.

REDES NEURONALES PARA MODELAR PREDICCIÓN DE HELADAS

A B S T R A C T In this work models based on neural networks of the backpropagation type were developed in order to predict the occurrence of frosts from meteorological data such as temperature, relative humidity, cloudiness and wind direction and speed. The training and the validation of the networks were made on the basis of 24 years of meteorological data corresponding to the Rio Cuarto station, Cordoba, Argentina. These data were grouped as follows: 10 years for the training data set and 14 years for the validation data set. Different models were built to evaluate the performance of the networks when different numbers of input variables and/or neurons in the hidden layer are used, and the probabilities of success in the prediction results on considering different input variables. In the models used, the percentage of days with prediction error was 2%, approximately, for the 14 years of application; when effective frosts days are considered the percentage varies between 10 and 23%, for the same period. The simulation results demonstrated the good performance and the relevance of this methodology for the estimation of the behavior of non-linear phenomena like frosts.

Automated identification of novel amphetamines using a pure neural network and neural networks coupled with principal component analysis

A pure neural network (NN) and several neural networks coupled with principal component analysis (PC-NN) have been developed in order to identify illicit amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical, and forensic purposes. The NN system has as input variables 260 spectral data, representing absorption intensities measured for each normalized infrared spectrum at 260 wavenumbers 10 cm −1 apart. In the case of PC-NN systems, the original spectral data (absorption intensities) have been compressed with the principal component analysis method (PCA), the scores of the principal components (PCs) being the inputs of these systems. We have built nine PC-NN systems, which have a different number of input variables: 3PCs, 4PCs, 5PCs, 6PCs, 7PCs, 8PCs, 9PCs, 10PCs and 15PCs. All systems are specialized to distinguish between stimulant amphetamines (class code M), hallucinogenic amphetamines (class code T) and nonamphetamines (class code N). We are now presenting a comparison of the validation results obtained for the NN system and for the best PC-NN system based on the scores of the first nine PCs (9PC-NN). The NN system correctly classifies all the positive samples, as opposed to the 9PC-NN system, which is characterized by a true positive rate (TP) of 90.91%. The true negative rate (TN) obtained for the first system (83.33%) is slightly higher than in the case of the later system (82.71%). Thus, the NN system is more sensitive and selective than the 9PC-NN system. We are also presenting a spectroscopic analysis of the false negative samples obtained in the case of 9PC-NN system.

Optimum design of ship design system using neural network method in initial design of hull plate

Manufacturing of complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way to cut-down the processing cost. This paper presents a new method to classify surface plates effectively in the preliminary ship design using neural network. A neural-network-based ship hull plate classification program was developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in the ship design. In automation of hull plate classification, two different numbers of input variables are used. By observing the results of the proposed method, the effectiveness of the proposed method is discussed. As a result, high prediction rate was achieved in the ship design. Accordingly, to the initial design stage, the ship hull plate classification program can be used to predict the ship production cost. And the proposed method will contribute to reduce the production cost of ship.

Multidimensional and multideme genetic algorithms for the construction of fuzzy systems

In this paper, a real-coded genetic algorithm (GA) is proposed capable of simultaneously optimizing the structure of a system (number of inputs, membership functions and rules) and tuning the parameters that define the fuzzy system. A multideme GA system is used in which various fuzzy systems with different numbers of input variables and with different structures are jointly optimized. Communication between the different demes is established by the migration of individuals presenting a difference in the dimensionality of the input space of a particular variable. We also propose coding by means of multidimensional matrices of the fuzzy rules such that the neighbourhood properties are not destroyed by forcing it into a linear chromosome. The effectiveness of the proposed approach is verified by a variety of simulation examples and is compared with other fuzzy, neuro-fuzzy and fuzzy–genetic approaches in terms of the root-mean-square error (RMSE).

Fuzzy Control of a Pneumatic Actuator

Abstract This work describes the design and the behavior of a fuzzy control system for a pneumatic actuator, aimed at controlling the piston position and at enabling the system to accurately follow desired reference trajectories. A non-linear model of the system and three increasingly complex versions of the fuzzy controller are described in this paper, with a different number of input variables. It is shown that the three-variable and the fourvariable fuzzy controllers allow to accurately control the response of the system to a position step input and the four-variable fuzzy controller enables the system to follow a sinusoidal reference trajectory with great accuracy.