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.

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.

Using Feed Forward BPNN for Forecasting All Share Price Index

Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward back propagation neural network (BPNN) is the widely used network topology for forecasting stock prices indices. In this study, we attempted to find the best network topology for one step ahead forecasting of All Share Price Index (ASPI), Colombo Stock Exchange (CSE) by employing feed forward BPNN. The daily data including ASPI, All Share Total Return Index (ASTRI), Market Price Earnings Ratio (PER), and Market Price to Book Value (PBV) were collected from CSE over the period from January 2nd 2012 to March 20th 2014. The experiment is implemented by prioritizing the number of inputs, learning rate, number of hidden layer neurons, and the number of training sessions. Eight models were selected on basis of input data and the number of training sessions. Then the best model was used for forecasting next trading day ASPI value. Empirical result reveals that the proposed model can be used as an approximation method to obtain next day value. In addition, it showed that the number of inputs, number of hidden layer neurons and the training times are significant factors that can be affected to the accuracy of forecast value.

Feed-Forward Neural Network Based Petroleum Wells Equipment Failure Prediction

In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.

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.

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.

Real-time Feed-forward Force Compensation for Active Magnetic Bearings System Based on H∞ Controller

There are two kinds of unbalance vibrations—force vibration and displacement vibration due to the existence of unbalance excitation in active magnetic bearings（AMB） system. And two unbalance compensation methods—closed-loop feedback and open loop feed-forward are presented to reduce the force vibration. The transfer function order of the control system directly influencing the system stability will be increased when the closed-loop method is adopted, which makes the real-time compensation not easily achieved. While the open loop method would not increase the primary transfer function order, it provides conditions for real-time compensation. But the real-time compensation signals are not easy to be obtained in the open loop method. To implement real-time force compensation, a new method is proposed to reduce the force vibration caused by the rotor unbalance on the basis of AMB active control. The method realizes real-time and on-line force auto-compensation based on H∞ controller and one novel feed-forward compensation controller, which makes the rotor rotate around its inertia axis. The time-variable feed-forward compensatory signal is provided by a modified adaptive variable step-size least mean square（VSLMS） algorithm. And the relevant least mean square（LMS） algorithm parameters are used to solve the H∞ controller weighting functions. The simulation of the new method to compensate some frequency-variable and sinusoidal signals is completed by MATLAB programming, and real-time compensation is implemented in the actual AMB experimental system. The simulation and experiment results show that the compensation scheme can improve the robust stability and the anti-interference ability of the whole AMB system by using H∞ controller to achieve close-loop control, and then real-time force unbalance compensation is implemented. The proposed research provides a new control strategy containing real-time algorithm and H∞ controller for the force compensation of AMB system. And the stability of the control system is finally improved.

ADAPTIVE FEED-FORWARD COMPENSATOR FOR HARMONIC CANCELLATION IN ELECTRO-HYDRAULIC SERVO SYSTEM

Since the dead zone phenomenon occurs in electro-hydraulic servo system, the output of the system corresponding to a sinusoidal input contains higher harmonic besides the fundamental input, which causes harmonic distortion of the output signal. The method for harmonic cancellation based on adaptive filter is proposed. The task is accomplished by generating reference signals with frequency that should be eliminated from the output. The reference inputs are weighted by the adaptive filter in such a way that it closely matches the harmonic. The output of the adaptive filter is a harmonic replica and is injected to the fundamental signal such that the output harmonic is cancelled leaving the desired signal alone, and the total harmonic distortion （THD） is greatly reduced. The weights of filter are adjusted on-line according to the control error by using least-mean-square （LMS） algorithm. Simulation results performed with a hydraulic system demonstrate the efficiency and validity of the proposed adaptive feed-forward compensator （AFC） control scheme

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

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.

Load Shedding Strategy Based on Combined Feed-Forward Plus Feedback Control over Data Streams

In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.

Noise decomposition algorithm and propagation mechanism in feed-forward gene transcriptional regulatory loop

Feed-forward gene transcriptional regulatory networks, as a set of common signal motifs, are widely distributed in the biological systems. In this paper, the noise characteristics and propagation mechanism of various feed-forward gene transcriptional regulatory loops are investigated, including （i） coherent feed-forward loops with AND-gate, （ii） coherent feed-forward loops with OR-gate logic, and （iii） incoherent feed-forward loops with AND-gate logic. By introducing logarithmic gain coefficient and using linear noise approximation, the theoretical formulas of noise decomposition are derived and the theoretical results are verified by Gillespie simulation. From the theoretical and numerical results of noise decomposition algorithm, three general characteristics about noise transmission in these different kinds of feed-forward loops are observed, i） The two-step noise propagation of upstream factor is negative in the incoherent feed-forward loops with AND-gate logic, that is, upstream factor can indirectly suppress the noise of downstream factors, ii） The one-step propagation noise of upstream factor is non-monotonic in the coherent feed-forward loops with OR-gate logic, iii） When the branch of the feed-forward loop is negatively controlled, the total noise of the downstream factor monotonically increases for each of all feed-forward loops. These findings are robust to variations of model parameters. These observations reveal the universal rules of noise propagation in the feed-forward loops, and may contribute to our understanding of design principle of gene circuits.

Feed-Forward-Like Decoupling Control in Coagulation Bath of Carbon Fiber Precursor

The coagulation bath system of carbon fiber precursor is a complicated and multivariable coupling system. Based on the model of industrial production,the full dynamic decoupling control of the coagulation bath system of carbon fiber precursor is achieved in combination with multivariable feed-forward-like decoupling and proportional-integral-differential( PID) control. Compared with the conventional PID decoupling control,the experiment results show that the proposed method has a better control effect. The use of the controller can achieve complete decoupling of three parameters from coagulation bath system. The method should have great applications.

基于超前滞后环节附加前馈阻尼的VSG控制策略

虚拟同步发电机(virtual synchronous generator,VSG)在引入同步机二阶转子运动方程,增大电力系统等效惯量的同时,也引入了同步发电机的振荡特性,有功低频振荡等动态稳定性问题也随之而来。引入调速侧电力系统稳定器(governor power system stabilizer,GPSS)能有效抑制VSG的功率低频振荡,但其在超调量及调节时间方面的控制效果仍有待提高。通过建立VSG的小信号模型从极点配置角度分析其稳定性,揭示基于GPSS的VSG控制策略在功率动态响应上存在较高超调和较长调节时间的原因。基于此,参考GPSS控制思想,提出了一种基于超前滞后环节附加前馈阻尼补偿的虚拟同步发电机控制策略。并从理论上分析验证了所提控制策略在不影响系统稳态特性的前提下,能够提供调整自由度更高的正阻尼,在有效地抑制功率超调的同时提高了系统的调节速度,从而更好地抑制了有功功率的低频振荡。最后通过MATLAB/Simulink进行对比仿真,仿真结果与理论分析结果一致,证明了所提控制策略的正确性和有效性。

无线电能传输系统最大效率追踪及恒压输出复合控制方法

针对无线电能传输(WPT)系统的传输效率受耦合线圈之间的互感以及负载影响的特点,该文提出一种基于阻抗匹配技术的最大效率追踪及基于前馈PI控制的恒压输出复合控制方法。首先,对双边LCC型WPT系统的参数和传输效率进行分析,通过调整系统参数优化系统的传输效率。其次,在二次侧使用DC-DC变换器采用阻抗匹配的方法实现最大效率追踪,同时在一次侧采用DC-DC变换器利用前馈PI控制器闭环控制负载端电压实现恒压输出。该方法中,效率追踪和电压控制之间相互独立,互不干扰。此外,该方法还通过系统工作时的电路参数来估算线圈间的互感值,并通过线性拟合的方法对该估算互感值进行修正,得到更精确的互感估算值。最后,通过搭建实验平台验证了该方法的可行性和有效性。与PI控制相比,前馈PI控制方法在快速性和抗扰动性上均具有明显优势。

双闭环控制的三相电压型PWM整流器的研究

针对传统的不控整流电路中网侧电流谐波很大、晶闸管相控整流电路中深控时网侧功率因数低和闭环控制时动态响应慢等问题,设计出一种引入脉冲宽度调制技术的三相电压型整流器。在完成三相电压型整流器的电压外环电流内环的双闭环控制系统设计的基础上,对其进行了仿真实验。仿真结果表明,与不控或相控整流电路相比,该三相电压型PWM整流电路中,不仅输入电流的谐波大大减小,电能可双向流动,能够实现整流和逆变状态下的单位功率因数运行,还可较好地抑制负载突变时直流侧电压的波动,从而显著提高系统的抗扰能力,真正实现绿色环保和高效节能的高度结合。

车用永磁同步电机无电流传感器控制研究

针对电动汽车车载环境复杂多变,影响电流传感器测量精度,更恶劣情况会导致电机驱动系统一相或多相电流传感器发生故障失效问题,因此基于扩展卡尔曼滤波提出一种无电流传感器控制算法,利用永磁同步电机定子电压、转子位置和转速信息重构电机定子电流,并针对无电流传感器算法导致的系统延迟问题设计了前馈补偿环节来改善系统动态性能,并对所提算法进行加减速及鲁棒性实验,仿真及实验结果均验证了所提方法的有效性。

单相级联H桥整流器平方电压反馈控制算法

以单相级联H桥整流器为研究对象,对其控制策略进行了研究。首先在采用双闭环控制策略的基础上,构建了dq前馈解耦模型,并通过二阶广义积分算法改善了虚拟交流分量对系统的影响,实现了网侧电流对电压相位、频率的快速精确追踪。其次,对传统电压平衡控制算法进行了改进,通过对功率平衡关系的定义,将输出电压平方作为控制信号,增强了系统自适应能力,提高了系统在投切载时的动态性能。最后,基于MATLAB/Simulink软件进行仿真,验证了所提策略的正确性和有效性。

面向56 Gb/s高速SerDes接收机DSP设计

高速接口芯片是高性能互连网络通信中的一款重要IP,针对56 Gb/s四脉冲幅度调制信号在高性能互连网络背板通信中,由于传输距离长信道衰减严重导致误码率高的问题,提出一种面向56 Gb/s高速Serdes接收机DSP设计。该DSP采用64路并行结构,通过16抽头前向反馈均衡器,以及1抽头预判决反馈均衡器对接收端数字化后的信号进行处理;采用基于K-均值聚类算法生成动态变化的判决电平并结合最小均方误差算法,能够处理15~35 dB不同信道衰减下的均衡问题。为了验证算法的性能,还搭建了一个基于模拟前端芯片和现场可编程门阵列的实验验证平台。实验结果表明,在信道衰减为15~35 dB@14 GHz,速率为56 Gb/s的条件下,误码率均小于5e-10。