Resilient Backpropagation Neural Network Research Articles

Temperature behavior modeling based on resilient BPNN for a GaAs pHEMT high gain MMIC PA

AbstractAs the key component of transceiver, power amplifier (PA) plays an extremely important role in wireless communication system. In order to accurately characterize the performance variation of PA at any temperature, the temperature behavior of a gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) high gain monolithic microwave integrated circuit (MMIC) PA is modeled in this paper. In this modeling, the resilient back propagation neural network (BPNN) is utilized to do the temperature behavior modeling for this PA. The investigation shows that the minimum mean square error (MSE) of the prediction results is 4.9607 × 10−4, which implies that it is feasible to use this modeling method to characterize the temperature behavior of PA. This modeling not only theoretically overcomes the problem of slow convergence speed of BPNN, but also solves the limitation of experimental devices and time setting. It will provide a more convenient guidance for test engineers.

Predictive Modeling of Yarn Quality at Ring Spinning Machine using Resilient Back Propagation Neural Networks

The final attenuation and twisting of fiber take place at ring spinning machine and hence its optimized performance is very crucial in terms of yarn quality. Drafting at ring spinning machine has a decisive effect on quality. There exist many influencing parameters in the spinning geometry that have to be optimized for manufacturing of quality yarn. The present research work was carried out to develop the Artificial neural networks (ANN) based prediction model for the polyester/cotton blended ring spun yarns by using these influencing parameters as inputs. ANN prediction model was developed using resilient backpropogation algorithm. Yarn quality parameters like yarn evenness, hairiness and tensile parameters were predicted. The low mean absolute error values for the yarn quality parameters proved that it is possible to predict the yarn quality on the basis of spinning geometry for cotton/polyester blended ring spun yarns using Resilient Back Propogation Neural Networks.

Power quality improvement by PV integrated UPQC using multi-level inverter with resilient back propagation neural network approach

To increase the life and efficiency of power electronics equipment in a utility distribution system, the power quality improvement is essential part. In this work, to improve power quality by using Robust Resilient Back Propagation Neural Network (RBPNN) scheme for a Photovoltaic (PV)-Integrated Unified Power Quality Conditioner (UPQC) with cascaded multi-level inverter configurations are described. Among the proposed methods, there is no need to use a transformer and filter when multilevel UPQC is applied, and it is one of the great advantages. The proposed UQPC offers a PV array composition with a power converter connected to a DC-link capacitor that can compensate for voltage sag, swell, voltage interruption, harmonics and reactive power. The Robust Resilient Back Propagation Neural Network controller is generate gating pulses to the UPQC. The reference currents and voltages for the controller are estimated using Synchronous Reference Frame (SRF) theory. The proposed cascaded multi-level inverter-based UPQC is designed using Matlab/Simulink Software. The simulation results confirm that the proposed method gives good results compared with existing Adaptive neural Fuzzy Inference System (ANFIS) and fuzzy logic methods. A real-time hardware system is also established to validate the simulation results. The effectiveness of the proposed system RBPN-UPQC approach is compared for both simulation and experimental results gives better low THD level 1.22%.

Performance enhancement of multi-port bidirectional DC-DC converter using resilient backpropagation neural network method

The world's energy consumption has been growing at an exponential rate. Traditional energy sources such as coal, oil, and gas must be replaced by renewable energy sources which are readily available and non-polluting. The combined solar photovoltaic and hybrid systems have several advantages including consistent output power during the daytime and at night. Three significant parts of a hybrid energy system are the power generation system, energy storage (batteries) and power conditioning modules. A new multi-port DC-DC power converter is proposed in this research to deal with the intermittent nature and slow responsiveness of renewable energy applications. In addition to the energy storage unit, the proposed converter incorporates a DC-DC converter, and the suggested circuit incorporates several renewable energy sources. When the primary energy supply is unavailable, battery storage devices store extra power and discharge it to meet peak power demand. The load is connected to two input ports and one output port in a three-port converter. The two input ports, namely photovoltaic and battery storage systems, are coupled to the first and second ports. This research work is based on the split-type secondary port for hybrid energy systems. The secondary port consists of both energy storage and solar energy source and both are switched alternatively using a bi-directional switching topology. The improvements in voltage boost ratio, output power and efficiency are presented in the proposed work. By regulating the Maximum Power Point Tracking, Resilient Back Propagation and Adaptive Neuro-Fuzzy Interference (ANFIS) approaches are utilised to harvest maximum power from the photovoltaic system (MPPT). The suggested hybrid energy system uses an isolated converter topology to isolate the input from the load. The Adaptive Neuro-Fuzzy Controller is realized using the Back Propagation Method which obtains a good result in voltage transfer ratio and net efficiency.

Resilient Back Propagation Neural Network Security Model For Containerized Cloud Computing

Cloud-native computing is getting more and more popular in recent years where containerized microservices architectural designs play a central role in building a distributed systems and services. On one hand, they bring convenience and simplicity to build massively scalable distributed cloud-native applications and enable continuous development and delivery for their services. On the other hand, they widen the surface of malicious intrusions, which, in turn, without proper defense mechanisms, lessens their benefits to a certain degree. Among the biggest threats of malicious intrusions are those that belong to the Distributed Denial of Service (DDoS) family. Such type of attacks are challenging because DDoS attacks are elevated hard-to-absorbed threats and have a high degree of variability in types, design, and complexity. In this work, resilient backpropagation neural network was used to build an intelligent network intrusion detection model against the most modernistic DDoS attacks in the cloud-native computing environment. We evaluated our proposed model using the benchmarking Canadian Institute for Cybersecurity evaluation CICDDoS 2019 dataset. Our proposed detection model has achieved high reflective DDoS attack detection. Therefore, it is appropriate to defend against reflective DDoS attacks in containerized cloud-native platforms. Experimental results indicate that the DDoS attack detection accuracy of the proposed resilient neural network model is as high as 97.07% which outperforms most of the well-known learning models mentioned in the most related work. Moreover, the proposed model has achieved a competitive run time performance that highly meets the delay requirements of containerized cloud computing.

Beyond site suitability: Investigating temporal variability for utility-scale solar-PV using soft computing techniques

• Resource variability investigation is beneficial for strategic planning. • Soft computing techniques are helpful to understand solar resource variability. • Model complexity is not concomitant to increased model performance. Site suitability is highly essential for utility-scale solar-PV exploration, however, beyond this phase, the need to investigate solar resource variability in suitable site is very vital to strategic planning and grid-integration. This study investigates solar resource variability in a suitable site obtained from geographical information system (GIS)-based site suitability analysis using soft computing techniques. The Western Cape Province, South Africa was used as a case study and climatological, topographic, and location factors were considered for the site suitability analysis for solar-PV in the province. Five soft computing techniques were applied on a candidate site chosen from the final suitability map. These are: gradient descent with adaptive learning rate neural network (GDALRNN), resilient backpropagation neural network (RBNN), adaptive neurofuzzy inference system (ANFIS), genetic algorithm-based ANFIS (GA-ANFIS) and particle swarm optimization based ANFIS (PSO-ANFIS). Each model was trained and tested with 70% and 30% of the data obtained from the representative site respectively. The GDALRNN model outperforms all other models with a root mean square error (RMSE) of 0.0316, coefficient of variation of RMSE (CV(RMSE)) of 4.7590%, relative mean bias error of −0.2951%, mean absolute percentage error of 4.0998, robust coefficient of variation of 0.0403, skill score of 10.7345 and computational time of 9.71 s. Finally, the possibility of integrating resource variability investigation into site suitability and the relevance of simple intelligent models over complex hybrid ones in solar resource variability modelling were established.

Smart IOT based Short Term Forecasting of Power Generation Systems and Quality Improvement Using Resilient Back Propagation Neural Network

In modern times, the instant improvement of changeable energy generations especially as of wind and solar energy resources in the power grid led with these generations developing a significant resource of ambiguity with behavior of load however living being the major variable source. It is crucial for the economic scheduling in the generation and load balance of the generating units and in trades of electricity market. The forecasting of energy managed to alleviate several challenges to encounter which rises the resource uncertainty. Solar and wind power prediction is observing a intensifying interest from the community research and numerous investigations working on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. Therefore, this work presents a Resilient Back Propagation Neural Network (RBPN) model to produce solar and wind power Short Term Forecasting (STF) and monitoring using Internet of Things (IoT).However, STF is very complex to handle due to the random and nonlinear characteristics of solar irradiance and wind speed under changeable weather conditions. But the proposed Resilient Back Propagation Neural Network (RBPN) is suitable for STF modeling and also the proposed forecasting system is directly connected to IEEE-9 bus to reduce Total Harmonics Distortion (THD) and also reduce the power quality issues in various conditions, such as voltage unbalance control, active and reactive power control. The performance of proposed forecasting system is validated through both hardware and simulation, the simulation is developed by using Matlab Simulink software. In the proposed system, the sensitivity analysis for varied variables and the model comparison with the aspect of proper selection incorporating the persistence and multiple linear regression models. Therefore, execution of the Internet of Things (IoT) in the supervising of solar PV and wind forecasting system was recommended and its implementation was analyzed. Suggested system comprised of data acquisition, data gateway, and smartphone application demonstration.

Multi‐objective Deng's grey incidence analysis, orthogonal optimization, and artificial neural network modelling in hot‐maceration‐assisted extraction of African cucumber leaves (Momordica balsamina)

AbstractDue to the inherent multiple response characteristics in many biological and separation processes, parameter optimization and modelling is usually a daunting task. The integration of Deng's grey incidence model (GRA) and Taguchi optimization (TM) therefore helps in transforming multiple quality characteristics into a single response presented as the grey relational grade (GRG). This was applied to optimize the multiple quality response characteristics in the maceration‐assisted extraction of African cucumber leaves. Two responses and five design factors were selected with L16(25) layout using signal‐to‐noise ratio as a point prediction feature. Under the optimized conditions, the optimum total phenolic content and antioxidant capacity of 0.8569 mg/ml gallic acid equivalence and 0.9259 mg/ml were achieved, respectively. The mass ratio was the highest contributor (38.2%), whereas the maceration time presented the least contribution (9.8%) to the cumulative response grade (GRG). In the neural network analysis, three models were deployed: Levenberg Marquardt backpropagation neural network (LMNN), gradient descent with adaptive learning rate neural network (GDALRNN), and the resilient back‐propagation neural network (RPNN). A better prediction of hold‐out data was achieved with the GDALRNN model, generating lesser absolute deviation error (MADGDALRNN = 0.099), root mean square error (RMSEGDALRNN = 0.1033), relative mean bias error (rMBEGDALRNN = − 0.24), and highest computational time (CTGDALRNN = 8.8), which is expected of an effective model. Based on the GRG and the signal‐to‐noise ratio, the optimum conditions and the neural network model succinctly provided a benchmark for future assessment of complex relationship among extraction variables, which could form the basis for a potential future scale‐up applications.

Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi

Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai” (BLT) and the “Program Keluarga Harapan” (PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%.

Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal

This work proposes a novel method for the detection of Left Ventricular Hypertrophy (LVH) from a multi-lead ECG signal. Left Ventricle walls become thick due to prolonged hypertension which may fail to pump heart effectively. The imaging techniques can be used as an alternative diagnose LVH; however, they are more expensive and time-consuming than proposed LVH. To overcome this issue, an algorithm to the diagnosis of LVH using ECG signal based on machine learning techniques were designed. In LVH detection, the pathological attributes such as R wave, S wave, inversion of QRS complex, changes in ST segment noticed in the ECG signal. This clinical information extracted as a feature by applying continuous wavelet transform. The signals were reconstructed with the frequency between 10 and 50 Hz from the wavelet. This followed by the detection of R wave and S wave peaks to obtain the relevant LVH diagnostic features. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Ensemble of Bagged Tree, AdaBoost classifiers were employed and the results are compared with four neural network classifiers including Multilayer Perceptron (MLP), Scaled Conjugate Gradient Backpropagation Neural Network (SCG NN), Levenberg–Marquardt Neural Network (LMNN) and Resilient Backpropagation Neural network (RPROP). The data source includes Left Ventricular Hypertrophy and healthy ECG signal from PTB diagnostic ECG database and St Petersburg INCART 12-Lead Arrhythmia Database. The results revealed that the proposed work can diagnose LVH successfully using neural network classifiers. The accuracy in detecting LVH is 86.6%, 84.4%, 93.3%,75.6%, 95.6%, 97.8%, 97.8%, 88.9% using SVM, KNN, Ensemble of Bagged Tree, AdaBoost, MLP, SCG NN, LMNN and RPROP classifiers, respectively.

A Multiperiod Multiobjective Portfolio Selection Model With Fuzzy Random Returns for Large Scale Securities Data

It is agreed that portfolio selection models are of great importance for the financial market. In this article, a constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the trading restrictions and quantifies future security returns by fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile, it considers terminal wealth, conditional value at risk (CVaR), and skewness as tricriteria for decision making. Obviously, the proposed model is computationally challenging. This situation gets worse when investors are interested in a larger financial market since the data they need to analyze may constitute typical big data. Whereafter, a novel intelligent hybrid algorithm is devised to solve the presented model. In this algorithm, the uncertain objectives of the model are approximated by a simulated annealing resilient back propagation (SARPROP) neural network which is trained on the data provided by fuzzy random simulation. An improved imperialist competitive algorithm, named IFMOICA, is designed to search the solution space. The intelligent hybrid algorithm is compared with the one obtained by combining NSGA-II, SARPROP neural network, and fuzzy random simulation. The results demonstrate that the proposed algorithm significantly outperforms the compared one not only in the running time but also in the quality of obtained Pareto frontier. To improve the computational efficiency and handle the large scale securities data, the algorithm is parallelized using MPI. The conducted experiments illustrate that the parallel algorithm is scalable and can solve the model with the size of securities more than 400 in an acceptable time.

Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism

ABSTRACT Precision agriculture involves manipulation of variations in field productivity, maximization of income, scale backing of wastes, and minimizing of the impact on surroundings using automated machine-controlled information assortment and documentation. This work focuses on the efficient control of farm irrigation by exploiting the capabilities of Internet of Things (IoT) and Big Data-based Decision Support System (DSS) to generate adequate valve control commands. Three varieties of prediction techniques such as Deep Neural Network (DNN), Random Forest (RF) and Resilient Back-Propagation Neural Network model are tested to predict soil Moisture Content (MC) in one hour advance by considering 6 numbers of different sensors. The real-time data collection is performed using the proposed IoT node deployment strategy tested in the field. An integrated IoT-based DSS framework is proposed to accumulate 17 numbers of soil and environmental parameters to predict future variation of soil MC in 1 h advance. Further, Structural Similarity (SSIM) Index is used to visualize and maintain uniform MC all over the agriculture area during the entire cropping period. Site and zone specific irrigation control scheme is tested in the test site using fuzzy logic-based weather dependent model. The complete system architecture, deployment strategy and performance of the proposed IoT-based DSS mechanism is discussed in this article.

Financial time series forecasting using Artificial Neural Networks

Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)

Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors. The total mutual fund asset globally as at the end of 2016 was in excess of $40.4 trillion. Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States, Luxembourg, Ireland, France, Australia, United Kingdom, Japan, China and Brazil. We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models. We selected six continuous historical years that is 2010–2011, 2012–2013 and 2014–2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier (FANNC), and to compare our results with those from an enhanced resilient back propagation neural networks (ERBPNN) model. Our FANNC model outperformed the existing models in terms of processing time and error rate. This makes it ideal for financial application that involves large volume of data and routine updates.

Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks

A method based on hedonic tone was developed and applied to evaluate human assessments of odor emitted from dairy operations using a fast gas chromatograph and neural networks. A general pleasantness scale ranging from −11 (extremely unpleasant) to +11 (extremely pleasant) was used to collect human responses. The panelists were able to identify the difference between various samples, and gave individually consistent responses for the same sample. The measurements of a fast gas chromatograph, called the zNose, were trained using Artificial Neural Networks (ANNs) to predict the human assessments. Three ANNs, Levenberg-Marquardt Back-propagation Neural Network (LMBNN), Scaled Conjugate Gradient Back-propagation (CGBNN), and Resilient Back-propagation Neural Network (RPBNN), were applied to connect human assessments and instrument measurements. In separate validation, zNose-LMBNN model showed superiority in four criteria, Mean Square Error (MSE), Correlation Coefficient (R), probability within 10% range to target, and probability within 5% range to target. The optimal model outputs represented human response as high as 67% within the 10% range and 44% within the 5% range of the targets. In addition, the model outputs have a good linear relationship with the targets (R = 0.53).

A method of crime rate forecast based on wavelet transform and neural network

Accurate prediction of crime is highly challenging. In order to improve efficiency of situational crime prevention, the temporal distribution of the crime rate within 24 hours was analysed and a forecast model combining discrete wavelet transform and resilient backpropagation neural network (DWT-RBPNN) is presented. First, historical crime incidence sequences obtained by the sliding window were decomposed by discrete wavelet transform. Then RBPNN trained decomposition sequences to predict the incidence of future trends and details. Finally, the trends and details were reconstructed to get the final prediction sequence. The experimental results showed that the proposed model has relatively high accuracy and feasibility on the crime rate prediction compared with single method of BPNN. The utility of the DWTRBPNN model can offer an exciting new horizon to provide crime rate forecasting and early warning in the situational crime prevention.

On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used topredict acceptance of Bidikmisi applicants yearly.

플럭스코어드와이어의 용접특성 예측을 위한 탄력적 오류역전파 신경망 모델에 관한 연구

플럭스코어드와이어의 용접특성 예측을 위한 탄력적 오류역전파 신경망 모델에 관한 연구

Prediction of Human Responses to Dairy Odor Using an Electronic Nose and Neural Networks

Abstract. Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative measurements from instruments. In this work, a prediction system was designed and developed to use instruments to predict human assessment of odors from common dairy operations. The model targets are the human responses to odor samples evaluated using a general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant). The model inputs were the electronic nose measurements. Three different neural networks, a Levenberg-Marquardt back-propagation neural network (LMBNN), a scaled conjugate gradient back-propagation neural network (CGBNN), and a resilient back-propagation neural network (RPBNN), were applied to connect these two sources of information (human assessments and instrument measurements). The results showed that the LMBNN model can predict human assessments with accuracy as high as 78% within a 10% range and as high as 63% within a 5% range of the targets in independent validation. In addition, the LMBNN model performed with the best stability in both training and independent validation. Keywords: Animal production, Hedonic tone, Olfactometric models.

An Efficient Patient Inflow Prediction Model For hospital Resource Management

<p><span style="font-size: small;">There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on.</span><span style="font-size: small;"> </span><span style="font-size: small;">These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. </span><span style="font-size: small;"> </span><span style="font-size: small;">This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.</span></p>