Design of speed controller for electronic fuel injection gasoline generator based on feed-forward PID control

As for the application of electronic fuel injection （EFI） system to small gasoline generator set, mechanical speed controller cannot be coupled with EFI system and has the shortcomings of lagged regulation and poor accuracy, a feed-forward control strategy based on load combined with proportional-integral-differential （PID） control strategy was proposed, and a digital speed controller applied to the electrical control system was designed. The detailed control strategy of the controller was intro- duced. The hardware design for the controller and the key circuits of motor driving, current sampling and angular signal captu- ring were given, and software architecture was discussed. Combined with a gasoline generator set mounted with EFI system, the controller parameters were tuned and optimized empirically by hardware in loop and bench test methods. Test results show that the speed deviation of generator set is low and the control system is stable in steady state; In transient state the control system responses quickly, has high stability under mutation loads especially when suddenly apply and remove 100% load, the speed deviation is within 8% of reference speed and the transient time is less than 5 s, satisfying the ISO standard.

Near-infrared Spectral Detection of the Content of Soybean Fat Acids Based on Genetic Multilayer Feed forward Neural Network

In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature.

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

QFT control based on zero phase error compensation for flight simulator

To improve the robustness of high-precision servo systems, quantitative feedback theory （QFT） which aims to achieve a desired robust design over a specified region of plant uncertainty is proposed. The robust design problem can be solved using QFT but it fails to guarantee a high precision tracking. This problem is solved by a robust digital QFT control scheme based on zero phase error （ZPE） feed forward compensation. This scheme consists of two parts： a QFT controller in the closed-loop system and a ZPE feed-forward compensator. Digital QFT controller is designed to overcome the uncertainties in the system. Digital ZPE feed forward controller is used to improve the tracking precision. Simulation and real-time examples for flight simulator servo system indicate that this control scheme can guarantee both high robust performance and high position tracking precision.

A simple and robust speed control scheme of permanent magnet synchronous motor

This paper presents a simple and robust speed control scheme of Permanent Magnet Synchronous Motor (PMSM). It is to achieve accurate control performance in the presence of load torque and plant parameter variation. A robust disturbance cancellation feed forward controller is used to estimate the torque disturbance. The simple and practical control scheme is easily implemented on a PMSM driver using a TMS320LF2407 DSP. The effectiveness of the proposed robust speed control approach is demonstrated by simulation and experimental results.

Frequency Support from PMSG-Based Wind Turbines with Reduced DC-Link Voltage Fluctuations

Frequency droop control is widely used in permanent magnet synchronous generators(PMSGs)based wind turbines(WTs)for grid frequency support.However,under frequency deviations,significant DC-link voltage fluctuations may occur during the transient process due to sudden changes in real power of such WTs.To address this issue,a current feedforward control strategy is proposed for PMSG-based WTs to reduce DC-link voltage fluctuations when the WTs are providing frequency support under grid frequency deviations.Meanwhile,the desired frequency support capability of the PMSG-based WTs can be ensured.Simulation results verify the rationality of the analysis and the effectiveness of the proposed control method.

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl2O3 and Pd/γAl2O3 disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (ø) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners was scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three-layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate the effects of coded R and X input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181 - 0.9809 & 634.5 - 3528.8 for RSM method compared to 0.9857 - 0.9951 & 7636.4 - 24,028.4 for ANN method beside lower values for error analysis terms.

Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning

The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their opponents.In the past,the focus was on improved training techniques for better physical performance.These days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players.Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past.The impact of a smart player on the success of a team is always a big question mark before the start of a match.The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player.Machine learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole team.The statistics are very vital and useful for management,fans,and expert analysis.In our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s performance.The data is collected from football websites,and the results are stored in the cloud for fast fetching of data.They can be retrieved anywhere in the world through cloud storage.The results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team.

Reconstruction of Turbulent Swirling Flow in a Dump Combustor

Experimental data of the continuous evolution of fluid flow characteristics in a dump combustor is very useful and essential for better and optimum designs of gas turbine combustors and ramjet engines. Unfortunately, experimental techniques such as 2D and/or 3D LDV （Laser Doppler Velocimetry） measurements provide only limited discrete information at given points; especially, for the cases of complex flows such as dump combustor swirling flows. For this type of flows, usual numerical interpolating schemes appear to be unsuitable. Recently, neural networks have emerged as viable means of expanding a finite data set of experimental measurements to enhance better understanding of a particular complex phenomenon. This study showed that generalized feed forward network is suitable for the prediction of turbulent swirling flow characteristics in a model dump combustor. These techniques are proposed for optimum designs of dump combustors and ramjet engines.

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling:Extra tree compared with feed forward neural network model

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.

Improved pilot data aided feed forward based on maximum likelihood for carrier phase jitter recovery in coherent optical orthogonal frequency division multiplexing

Pilot data aided feed forward （PAFF） carrier recovery is essential for phase noise tracking in coherent optical receivers. This paper describes a new PAFF system based on new pilot arrangement and maximum likelihood （ML） to estimate the phase jitter in coherent receiver- induced by local oscillator＇s lasers and sampling clock errors. Square M-ary quadrature amplitude modulation （M-QAM） （4, 16, 64, and 256） schemes were used. A detailed mathematical description of the method was presented. The system performance was evaluated through numerical simulations and compared to those with noisefree receiver （ideal receiver） and feed forward without ML. The simulation results show that PAFF performs near the expected ideal phase recovery. Results clearly suggest that ML significantly improves the tolerance of phase error variance. From bit error rate （BER） sensibility evaluation, it was clearly observed that the new estimation method performs better with a 4-QAM （or quadrature phase shift keying （QPSK）） format compared to three others square QAM schemes. Analog to digital converter （ADC） resolution effect on the system performance was analyzed in terms of Q-factor. Finite resolution effect on 4-QAM is negligible while it negatively affects the system performance when M increases.

An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

STUDYING THE ABRASION BEHAVIOR OF RUBBERY MATERIALS WITH COMBINED DESIGN OF EXPERIMENT-ARTIFICIAL NEURAL NETWORK

In this study, an application of artificial neural network （ANN） has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were carried out in which the experiment data were collected according to classical response surface designs. Besides developing the ANN models, we developed response surface methodology （RSM） to confirm the ANN predictions. A simple relation was employed for determination of relative importance of each variable according to ANN models. It was shown through these case studies that ANN models delivered very good data fitting and their simulating curves could help the researchers to better understand the abrasion behavior.

Proton exchange membrane fuel cell voltage-tracking using artificial neural networks

Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells.The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature,pressure,and flow rate.To overcome this problem,fast and delay-free systems are necessary for predicting control signals.In this paper,we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance.This is done through an input air pressure control signal.The proposed artificial neural network was constructed based on a back propagation network.A fuel cell nonlinear model,with and without feed forward control,was investigated and compared under random current variations.Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage.Also,less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.

Modeling of the RF system for the normal conducting linac

To study the new RF control methods, a mathematic model of the RF system for the normal conducting linac is built and implemented with the software of Matlab. The model contains some typical units of the RF system, such as the klystron, the SLED and the traveling wave accelerating tube. Finally, the model is used to study the working point of the SLED and the adaptive feed forward algorithm for the RF control system. Simulation shows that the model works well as expected.

Artificial intelligence model for predicting geomechanical characteristics using easy-to-acquire offset logs without deploying logging tools

This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value.