Adaptive Artificial Neural Network Model Research Articles

A self-organizing map clustering adaptive artificial neural network model for analysis critical success factors in different phases of MTO projects case study: electrical equipment manufacturer and supplier in Iran

PurposeThe purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.Design/methodology/approachThis study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.FindingsCluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.Research limitations/implicationsThe main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.Practical implicationsIn this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.Originality/valueThis paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.

Development of an adaptive artificial neural network model and optimal control algorithm for a data center cyber–physical system

This study aimed to develop an adaptive artificial neural network model (AAM) for the prediction of the rack inlet temperature and cooling system energy, and the optimal control algorithm for cooling system of a containment-type data center. A cyber-physical system (CPS) framework, that incorporated the AAM and control algorithm, was also proposed for the precise control of the data center cooling system. To develop the AAM model and control algorithm, mathematical modeling of a reference physical model was conducted, and training data were acquired from this model. The performance of the proposed AAM and control algorithm was then compared with that of a non-adaptive ANN model (NAAM) in terms of prediction accuracy and control stability. The analysis results indicated that the optimal control algorithm with the AAM exhibited superior prediction accuracy and control stability than the algorithm with the NAAM. In particular, for the AAM-based algorithm under conditions representing a novel data center environment, the root mean square error (RMSE) and coefficient of variation of the RMSE (CV(RMSE)) for the predicted and actual values were 0.22 °C and 1.02%, respectively, for the inlet rack temperature and 0.19 kW and 0.76% for the cooling system energy. The control was also stable, with an MAE of 0.08 °C and a maximum error of 1.17 °C. Based on this analysis, a CPS-based control strategy incorporating an ANN-based optimal control algorithm is expected to be an effective energy efficiency solution for existing data center without changing IT equipment or cooling systems.

Monitoring and prediction of drought using TIBI fuzzy index in Iran

The drought phenomenon is not specific to the region, affecting different parts of the world. One of these areas is Iran in Southwest Asia, suffering from this phenomenon in recent years. The purpose of this study was to model, analyze and predict the drought in Iran. So that, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations during 29 years (1990-2018). For modelling the TIBI fuzzy index, at first, four indicators (SET, SPI, SEB, and MCZI) were been fuzzy in MATLAB software. Then the indices were compared and the TOPSIS model was used for prioritizing areas involved drought, followed by employing ANFIS adaptive artificial neural network model for predicting drought. Results showed that the new fuzzy index TIBI for classifying drought reflected four above indicators with high accuracy. Of these five climatic parameters used in this study, the temperature and precipitation exhibited the most impact on the fluctuation of drought severity. The severity of drought was more based on 6-month scale modelling than on 12-month one. The highest rate of drought occurrence was found at the Bandar Abbas station with 24.30% on a 12-month scale, and while the lowest was at the Shahrekord station with 0.36% on a six-month scale. Based on ANFIS model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more encountered ones to drought due to the TIBI index of 0.62, 0.96 and 0.97 respectively. According to the results in both 6- and 12-month scales, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.

Innovative Scaled Hardware Simulator for Designing and Testing an EV’s Battery Storage System Incorporated with an Adaptive ANN Model

In this study, a scaled-down system, which can be used as a benchmark test for the battery storage designing of electric vehicles (EVs) is proposed. This model was based on the hardware simulator of the battery storage system (BSS) used from a single cell up to 4 cells in a series pack system, which simulates a practical battery pack. The developed simulator can charge and discharge any rechargeable battery, such as Li-Ion, Ni–MH or Pb battery. The scaling ratio of the simulator was evaluated by the ratio of the current or power of the battery pack specimen related to the specification. Also, this study proposes an innovative state of charge (SoC) estimation of the battery pack for EVs based on genuine results obtained through practical tests. This estimation was carried out by an adaptive artificial neural network (ANN) algorithm, using simple inputs. As well, this model can deduct the state of health (SoH) of the battery pack based on the power output level and waveform characteristics. The results of the ANN showed high generalization, a low error of SoC estimation at the level of 1.1%, with a calculation time less than 16.5 s. Regarding the hardware simulator, the similarity of the results and waveform accuracy of the scaled-down battery systems compared with the real battery pack was very acceptable with a maximum deviation of 2.1% in the worst scenario. The cells cycled with different depths of discharge (DoD) or C-rates, at different temperatures with different initial SoCs using any arbitrary current waveforms. Our conclusions will help battery manufacturers to test and evaluate the performance of the BSS in different applications, such as EVs, PV generation, and wind farm, with significant cost reduction. Also, the ANN algorithm can be used and embedded in EVs or in any other industrial application, as proposed in this paper. This study contributed to the real-time diagnosis of the BSS without interrupting the normal operation based on its features.

A novel clustering-enhanced adaptive artificial neural network model for predicting day-ahead building cooling demand

To accurately predict hourly day-ahead building cooling demand, year-round historical weather profile needs to be evaluated. The daily weather profiles among different time periods result in various features of historical datasets. The different appropriate structure and parameters of artificial neural network models may be identified for training datasets with different features. In this study, a novel clustering-enhanced adaptive artificial neural network (C-ANN) model is proposed to forecast 24h-ahead building cooling demand in subtropical areas. The uniqueness of the proposed adaptive model is that k-means clustering is implemented to recognise representative patterns of daily weather profile and thus categorize the annual datasets into featuring clusters. Each cluster of the weather profile, along with the corresponding time variables and cooling demand, is adopted to train one ANN sub-model. The optimal structure and parameters of each ANN sub-model are selected according to its featuring training datasets; thus the ANN sub-models are adaptive. The proposed C-ANN model is tested on a representative office building in Hong Kong. It is found that the mean absolute percentage error of the training and testing cases of the proposed predictive model is 3.59% and 4.71%, which has 4.2% and 3.1% improvement compared to conventional ANN model with a fixed structure. The proposed adaptive predictive model can be applied in building energy management system to accurately predict day-ahead building cooling demand using the latest forecast weather profile.

Modelling, evaluation and simulation of drought in Iran, southwest Asia

The drought phenomenon is not specific to a region and it affects different parts of the world. One of these areas is Iran in southwest Asia, which suffered from this phenomenon in recent years. The purpose of this study is to model, analyze and predict the drought in Iran. To do this, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations for a period of 29 years (1990–2018). For modelling of the Combined Indicateurs based on four indices, Standardized Evapotranspiration Torrent White Index (SET), Standardized Precipitation Index (SPI), Standardized Evapotranspiration Blanney Creedal FAO Index (SEB) and Modified CZI Index (MCZI) were fuzzy in Matlab software. Then the indices were compared and the Topsis model was used for prioritizing areas involved with drought. Finally, Anfis adaptive artificial neural network model was used to predict. Results showed that the new fuzzy index TIBI for classifying drought reflected above four indicators with high accuracy. Of these five climatic parameters: (precipitation, temperature, sunshine, minimum relative humidity and wind speed) used in this study, the temperature and precipitation parameters had the most effect on the fluctuation of drought severity. The severity of the drought was more based on 6-month scale modelling than 12 months. The highest percentage of drought occurrence was at Bandar Abbas station with a value of 24.30 on a 12-month scale and the lowest was in Shahrekord station with a percentage of 0.36% on a 6-month scale. Based on Anfis model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more exposed to drought due to the TIBI index of 0.62, 0.96 and 0.97, respectively. According to the results in both 6 and 12 months scale, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.

Development of energy saving technique for setback time using artificial neural network

ABSTRACT This study is desired to form an adaptive artificial neural network (ANN)-based model for optimal setback period application, thus predicting the suitable time for setback temperature deployment. A TRNSYS–MATLAB co-simulation platform is formulated to simulate a given building envelop with the application of desired control. Here, a single building model was simulated under five different climatic conditions of Taxila (Pakistan), Mogadishu (Somalia), Darwin (Australia), Phoenix (USA) and Havana (Cuba). Five influencing parameters were selected for the study, and using analysis of variance test, it was found that inside air condition is the most influencing parameter for the setback time period. Initially, one model was used for all five climates. Parametrical tuning optimisation was used for making ANN model optimal for all five climatic regions. This made five optimal ANN models separately for all five weathers. Three peak summer months were selected for all five climatic zones in which first two months were used for ANN training and last one month was used for ANN learning. ANN models prediction accuracy was computed by determination coefficient (R 2) and by coefficient of variation for root mean square error (CV for RMSE) . R 2 of ANN control algorithm was as follow: 0.9754 for Taxila, 0.9721 for Mogadishu, 0.8934 for Darwin, 0.9609 for Phoenix and 0.9978 for Havana, while CV for RMSE for Taxila, Mogadishu, Darwin, Phoenix and Havana was 9.88%, 8.83%, 19.49%, 11.34% and 3.98%, respectively. The ANN control algorithm appeared to be energy efficient, saving 12.5%, 2.4%, 8.4%, 2.0% and 18.2% (in kW h) for Taxila, Mogadishu, Darwin, Phoenix and Havana region, respectively, as compared to basic control algorithm.

Modeling, Monitoring and Prediction of Drought in Iran

The drought phenomenon is not specific to the region and it affects different parts of the world. One of these areas is Iran in Southwest Asia, which suffered from this phenomenon in recent years. The purpose of this study is to model, analyze and predict the drought in Iran. To do this, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations in the period of 29 years (1990-2018). For modeling of TIBI fuzzy index, first, four indicators (SET, SPI, SEB, MCZI) were fuzzy in Matlab software. Then the indices were compared and Topsis model were used for prioritizing areas involved with drought. Finally, Anfis adaptive artificial neural network model was used to predict. Results showed that the new fuzzy index TIBI for classifying drought reflected four of the above indicators with high accuracy. Among these five climatic parameters used in this study, the temperature and precipitation parameters had the most influential effect on the fluctuation of drought severity. The severity of drought was more based on a 6-month scale modeling than 12 months. The highest percentage of drought occurrence was at Bandar Abbas station with a value of 24.3 on a 12-month scale and the lowest was in Shahrekord station with a percentage of 0.36% on a six-month scale. Based on Anfis model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more exposed to drought due to the TIBI index of 0.62, 0.96 and 0.97, respectively. According to the results in both 6 and 12 months scale, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings

The aim of this study was to develop a control algorithm to demonstrate the improved thermal comfort and building energy efficiency of accommodation buildings in the cooling season. For this, two artificial neural network (ANN)-based predictive and adaptive models were developed and employed in the algorithm. One model predicted the cooling energy consumption during the unoccupied period for different setback temperatures and the other predicted the time required for restoring current indoor temperature to the normal set-point temperature. Using numerical simulation methods, the prediction accuracy of the two ANN models and the performance of the algorithm were tested. Through the test result analysis, the two ANN models showed their prediction accuracy with an acceptable error rate when applied in the control algorithm. In addition, the two ANN models based algorithm can be used to provide a more comfortable and energy efficient indoor thermal environment than the two conventional control methods, which respectively employed a fixed set-point temperature for the entire day and a setback temperature during the unoccupied period. Therefore, the operating range was 23–26°C during the occupied period and 25–28°C during the unoccupied period. Based on the analysis, it can be concluded that the optimal algorithm with two predictive and adaptive ANN models can be used to design a more comfortable and energy efficient indoor thermal environment for accommodation buildings in a comprehensive manner.

Reduction in impulse noise in digital images through a new adaptive artificial neural network model

In this paper, an adaptive artificial neural network model is developed in order to restore severely corrupted images. The proposed new and effective impulse noise reduction filter is named as adaptive neural network models with an algorithm based on artificial neural networks. Networks trained at different noise intensities get activated according to the intensity of the noise and estimate the most suitable neighboring pixel that can replace the corrupted pixel. The proposed algorithm reduces impulse noise effectively while also protecting the details. Experimental results show that the proposed algorithm performs better compared with other traditional filters.

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

This paper presents a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis. The survey identifies the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems. ANN models are then used in the reliability analysis of a ship stiffened panel subjected to uniaxial compression loads induced by hull girder vertical bending moment, for which the collapse strength is obtained by means of nonlinear finite element analysis (FEA). The approaches adopted combine the use of adaptive ANN models to approximate directly the limit state function with Monte Carlo simulation (MCS), first order reliability methods (FORM) and MCS with importance sampling (IS), for reliability assessment. A comprehensive comparison of the predictions of the different reliability methods with ANN based LSFs and classical LSF evaluation linked to the FEA is provided.

An adaptive neural network prediction for nonlinear parabolic distributed parameter system based on block-wise moving window technique

This paper proposes an efficient adaptive artificial neural network (ANN) model for nonlinear parabolic distributed parameter systems (DPSs) with changes in operating condition. To obtain the complex spatiotemporal dynamics of DPS, the ANN model is updated via applying block-wise recursive formula. The improved group search optimization (IGSO) approach is proposed to optimize the connection weights and thresholds of the ANN to solve the problem of falling into the local optima. Meanwhile, when the number of the new data does not reach the threshold of the block-wise, the ANN does not need to update. And, the predictive output consists of the ANN model predictive output and the compensated output obtained from the real time predictive errors by recursive least squares method. The proposed method can effectively capture the slowly changing of process dynamics and decrease the computational cost. Simulations are presented to demonstrate the accuracy and effectiveness of the proposed methods.

Artificial Neural Network for the Control of the Openings and Cooling Systems of the Double Skin Envelope Buildings

This study aimed at developing an artificial neural network (ANN)-based temperature control method for the double skin envelope buildings. For this, control logic for opening conditions of the inner and outer surfaces’ openings as well as for cooling system’s operation was developed based on the predictive and adaptive ANN model. The parametrical optimization process for the structure and learning methods of the ANN model was conducted in terms of the number of hidden layers, the number of neurons in the hidden layers, learning rate, and moment. Then, the performance of this optimized model was tested using the similarity analysis between the predicted values from the ANN model and the measured values from the actual double skin envelope building. Analysis revealed that the developed ANN model proved its prediction accuracy and adaptability in terms of stable Root Mean Square (RMS) and Mean Square Error (MSE) values. Based on this finding, it can be concluded that the developed ANN model showed potentials to be successfully applied to the temperature controls for the double skin envelope buildings.

Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure

This paper presents an adaptive artificial neural network (ANN) for modeling and simulation of a Stand-Alone photovoltaic (SAPV) system operating under variable climatic conditions. The ANN combines the Levenberg–Marquardt algorithm (LM) with an infinite impulse response (IIR) filter in order to accelerate the convergence of the network. SAPV systems are widely used in renewable energy source (RES) applications and it is important to be able to evaluate the performance of installed systems. The modeling of the complete SAPV system is achieved by combining the models of the different components of the system (PV-generator, battery and regulator). A global model can identify the SAPV characteristics by knowing only the climatological conditions. In addition, a new procedure proposed for SAPV system sizing is presented in this work. Different measured signals of solar radiation sequences and electrical parameters (photovoltaic voltage and current) from a SAPV system installed at the south of Algeria have been recorded during a period of 5-years. These signals have been used for the training and testing the developed models, one for each component of the system and a global model of the complete system. The ANN model predictions allow the users of SAPV systems to predict the different signals for each model and identify the output current of the system for different climatological conditions. The comparison between simulated and experimental signals of the SAPV gave good results. The correlation coefficient obtained varies from 90% to 96% for each estimated signals, which is considered satisfactory. A comparison between multilayer perceptron (MLP), radial basis function (RBF) network and the proposed LM–IIR model is presented in order to confirm the advantage of this model.

06/00131 An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria: Mellit, A. et al. Renewable Energy, 2005, 30, (10), 1501–1524

06/00131 An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria: Mellit, A. et al. Renewable Energy, 2005, 30, (10), 1501–1524

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria

In this paper we investigate, the possibility of using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. The model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) systems, we need to determine the optimal sizing coefficients ( K PV, K B). These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available. These coefficients are considered the most important parameters for sizing a PV system. Results obtained by classical models (analytical, numerical, analytical-numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR are compared with experimental sizing coefficients in order to illustrate the accuracy of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and handle a number of unusual cases. The unknown validation sizing coefficients set produced very accurate estimation with a correlation coefficient of 98%. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria. The methodology proposed in this paper however, can be generalized using different locations of the world.