Performance Of Neural Network Model Research Articles

Facilitating the implementation of neural network-based predictive control to optimize building heating operation

Simple neural network (NN) architecture is a reliable tool to transform reactive rule-based systems into predictive systems. Thermal comfort is of utmost importance in office buildings, which need the activation of heating systems at an optimal time. A high-performance NN predictive system requires a large training dataset. This can limit system efficiency due to the lack of enough historical data derived from thermal controllers. To address this issue, we generated, trained and tested a dataset of eight sizes using a calibrated building model. A set of key performance indicators (KPIs) was improved by studying the output performance. The effect of normalization and standardization preprocessing techniques on NN prediction ability was studied. Learning curves showed that a minimum of 1–4 months of data are required to obtain enough accuracy. Two heating seasons provide the optimal data size to calibrate the NN properly with high prediction accuracy. The results also revealed that building data from ≥two years slightly improve NN performance. The most accurate results in KPIs (≥ 90%) were obtained with preprocessed data. The effect of preprocessing on large training patterns was less than that of training patterns <100. Finally, NN model performance was less accurate in cold climate zones.

High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

EEG analysis aims to help scientists better understand the brain, help physicians diagnose and treatment choices of the brain-computer interface. Artificial neural networks are among the most effective learning algorithms to perform computing tasks similar to biological neurons in the human brain. In some problems, the neural network model's performance might significantly degrade and overfit due to some irrelevant features that negatively influence the model performance. Swarm optimization algorithms are robust techniques that can be implemented to find optimal solutions to such problems. In this paper, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms are applied for the feature selection and the training of a Feed-forward Neural Network (FFNN). The performance of the FFNN in terms of test accuracy, precision, recall, and F1_score is investigated. Furthermore, this research has implemented other five machine learning algorithms for the purpose of comparison. Experimental results prove that the neural network model outperforms all other algorithms via GWO.

Improving the Neural Network Testing Performance by Transforming the Normalized Data Nonlinearly for Trip Distribution Modelling

Previous studies have suggested that the Artificial Neural Network (NN) trip distribution models were unable to calibrate and generalize work trip numbers with the same level accuracy as the Doubly-Constrained Gravity models (DGCM). This study presents some new NN model forms aimed at overcoming these problems trained by using the Levenberg-Marquardt algorithm. A further modification was applied to the model, namely transforming the input data nonlinearly by using logistic functions (Sigmoid) in order to improve the testing/generalization of NN models. This resulted in better performance of NN models, where the average Root Mean Square Error (RMSE) is statistically lower than the DCGM indicating the NN models could have higher generalization ability than DCGM.

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output.

Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters

This paper describes a Neural Network (NN) model to classify healthy and mastitis Murrah buffaloes using pH, electrical conductivity, dielectric constant and yield of milk as input parameters and California Mastitis Test (CMT) score as the output parameter. The purpose of this study was to develop such a cost-effective and intelligent classification model, which would serve an alternative to the prevailing Somatic Cell Count (SCC) based techniques to detect mastitis in Murrah buffaloes, because the latter techniques are sophisticated, lengthy and time consuming as well as necessary instruments for carrying out the tests are not, generally, available at the grassroots level or to the small dairy holders. Accordingly, a total of 534 milk samples were collected from 100 lactating Murrah buffaloes, which were scrutinized for mastitis using CMT. The animals were classified into three categories, i.e., healthy, subclinical and clinical mastitis buffaloes and assigned CMT scores as 1, 2, and 3, respectively. The NN models were based on error back propagation learning algorithm with Bayesian regularization mechanism and various combinations of internal parameters. The performance of NN models was compared with that of conventional Multiple Linear Regression (MLR) models also developed in this study. The classification accuracy achieved by the best NN model was 8.02 Root Mean Square percent error (%RMS) while that attained by MLR model was 26.47 %RMS. Further, for classifying healthy vs. subclinical mastitis Murrah buffaloes, sensitivity, specificity and Diagnostic Odds Ratio (DOR) with the best NN model was found to be 98%, 97.72% and 54.87, respectively, having Area under Relative Operating Characteristic (ROC) Curve (AUC) as 0.96 vis-à-vis MLR model attaining the same as 58.87%, 76.72%, and 52.26, respectively, and AUC as 0.81. In case of classifying healthy vs. clinical mastitis Murrah buffaloes, the best NN model achieved sensitivity, specificity and DOR as 99%, 97.28% and 57.92, respectively, with AUC as 0.98 while that with MLR model were determined as 69.23%, 78.20% and 55.46, respectively, and AUC as 0.87. Evidently, the NN model outperformed classical MLR model, in this study. Hence, it can be deduced that NN paradigm has potential to efficiently detect healthy and mastitis Murrah buffaloes on the basis of milk yield and milk quality parameters.

Forecasting Project Performance Using a Neural Predictor Model

Recently a Project Assessment Indicator (PAI) Model has been applied in the Project Management Field. The PAI Model output or PAI indicates the entire project performance at the current but not at the future time. We use a neural network (NN) to predict the project performance based on past PAI data. The NN model has been tested on monthly PAI and virtual weekly PAI generated using the 2nd Newton Interpolation Function. Generating weekly PAI using our approach helps to increase the dataset size for training the neural predictor model and allows the project manager to make weekly predictions of project performance. The project manager may then have quantitative estimates of the future performance of the project at intervals shorter than one month. PAI is normally reported on a monthly basis. The NN model performance is evaluated using the Mean Absolute Percantage Error. We achieve a predictive accuracy ranging from 1.21% to 2.87% on the test data of the virtual weekly PAIs. This suggests that depending on the dataset size, project managers could use the NN model to predict future project performance to about 1.21% ∼ 2.87% of the weekly PAI value.