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.

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.

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.

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.

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.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

The Application of Artificial Neural Network in Assessing Chinese Mobile Internet Service

This paper pays its attention on Chinese mobile Internet service( MIS). Chinese MIS is developing so rapidly that the research on the mechanism of the formation of MIS assessment makes significant sense and therefore the three layers construct of the artificial neural network( ANN) theory is applied to address the problem. The final research model contains MIS features including personalization,localization,reachability,connectivity,convenience and ubiquity as the input layer variables,perceived MIS quality and MIS satisfaction as the hidden layer variables and reuse intention as the output layer variable. MIS risk is identified as the mediating variable. Theoretically,the framework is robust and reveals the mechanism of how customers evaluate a certain mobile Internet service. Practically,the model based on ANN should shed some light on how to understand and improve customer perceived mobile Internet service for both MIS giants and new comers.

Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle

This paper offer an artificial neural network (ANN) model to calculate drag force on an axisymmetric underwater vehicle by obtaining dataset from a computational fluid dynamic analysis. First, great effort was done to calculate the pressure and viscous data forces by increasing the precision and numerical data in order to extend and raise quality of dataset. In this step, numerous different geometry models (configurations of axisymmetric body) were designed, examined and evaluated input parameters including: diameter of body, diameter of nose disc, length of body, length of nose and velocity whereas outputs contain pressure and viscous forces. This dataset was used to train the ANN model. Feed-forward neural network (FFNN) is selected which is more common and suitable in this field’s study. A three-layer neural network was opted and after training this network, the results showed good agreement with CFD data. This study shows that applying the ANN model helps to reach final purpose in the least time and error, in addition a variety of tests can be performed to have a desired design in this way.

Wavelet based detection of ventricular arrhythmias with neural network classifier

This paper presents an algorithm based on the wavelet decomposition, for feature extraction from the Electrocardiogram (ECG) signal and recognition of three types of Ventricular Arrhythmias using neural networks. A set of Discrete Wavelet Transform (DWT) coefficients, which contain the maximum information about the arrhythmias, is selected from the wavelet decomposition. These coefficients are fed to the feed forward neural network which classifies the arrhythmias. The algorithm is applied on the ECG registrations from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases. We applied Daubechies 4 wavelet in our algorithm. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

New Approaches for Image Compression Using Neural Network

An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.

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.

Hardware Realization of Artificial Neural Network Based Intrusion Detection &Prevention System

In the 21st century with the exponential growth of the Internet, the vulnerability of the network which connects us is on the rise at a very fast pace. Today organizations are spending millions of dollars to protect their sensitive data from different vulnerabilities that they face every day. In this paper, a new methodology towards implementing an Intrusion Detection & Prevention System (IDPS) based on Artificial Neural Network (ANN) onto Field Programmable Gate Array (FPGA) is proposed. This system not only detects different network attacks but also prevents them from being propagated. The parallel structure of an ANN makes it potentially fast for the computation of certain tasks. FPGA platforms are the optimum and best choice for the modern digital systems nowadays. The same feature makes ANN well suited for implementation in FPGA technology. Hardware realization of ANN to a large extent depends on the efficient implementation of a single neuron. However FPGA realization of ANNs with a large number of neurons is still a challenging task. The proposed multilayer ANN based IDPS uses multiple neurons for higher performance and greater accuracy. Simulation of the design in MATLAB SIMULINK 2010b by using Knowledge Discovery and Data Mining (KDD) CUP dataset shows a very good performance. Subsequently MATLAB HDL coder was used to generate VHDL code for the proposed design that produced Intellectual Property (IP) cores for Xilinx Targeted Design Platforms. For evaluation purposes the proposed design was synthesized, implemented and tested onto Xilinx Virtex-7 2000T FPGA device.

比例延迟微分方程的极限学习机算法

目的针对比例延迟微分方程,提出一种基于极限学习机(ELM)算法的单隐藏层前馈神经网络训练方法,并将该方法推广到求解双比例延迟微分系统。方法首先,构建一个单隐藏层前馈神经网络并随机生成输入权值和隐藏层偏置;然后,通过计算系数矩阵使其满足比例延迟微分方程及其初值条件,将其转化为最小二乘问题,利用摩尔-彭罗斯广义逆解出输出权值;最后,将输出权值代入构建的神经网络便可获得具有较高精度的比例延迟微分方程数值解。结果通过数值实验与已有方法的结果进行比较,验证了该方法对处理比例延迟微分方程与双比例延迟微分系统的有效性,且随着选取的训练点和隐藏层节点数量增多,所得到的数值解精度和收敛速度也随之增加。结论ELM算法对处理比例延迟微分方程以及双比例延迟微分系统具有较好的效果。

BP神经网络在垃圾焚烧电厂酸性气体排放控制中的应用

对于采用半干法的大多数垃圾焚烧发电厂,控制酸性气体排放浓度的主要手段是调节脱酸塔入口石灰浆流量。传统的PID控制采用CEMS实测数据作为反馈信号调节石灰浆流量,但脱酸塔入口到烟囱烟气采样点距离远,反应时间长,控制效果不佳。为此,引入BP人工神经网络遗传算法,并利用人工控制模式下积累的大量过程数据训练人工神经网络,以替代人工控制。

基于时变深度前馈神经网络的风电功率概率密度预测

针对传统循环神经网络(RNN)和卷积神经网络(CNN)模型对风电功率进行较长时间尺度的短期预测时出现的时不变性问题,应用时变深度前馈神经网络(ForecastNet)模型进行短期风电功率不确定性预测。该模型的网络结构随时间变化以提高多步提前预测能力,模型交错输出以缓解梯度消失问题,使用混合密度网络得到各个时刻的概率密度分布。在避免传统深度学习模型中,该模型能避免递归多步预测累积误差的同时可以充分考虑相邻时刻风电功率的相关性;在模型隐藏层中,使用美国PJM网上的风电功率实际数据,分别应用全连接网络、卷积网络以及基于注意力机制的卷积网络3种神经网络模型进行预测,每次预测未来12 h的风电功率,滚动预测得到未来500 h的风电功率区间和概率密度,实验仿真结果能够证明所提预测模型的有效性。

基于前馈神经网络的分数阶事件触发控制器实现及应用研究

为了提高事件触发控制算法的普适性,降低现有控制算法对具体数学表达式的依赖,提出了一种基于前馈神经网络的事件触发控制方法,实现了更具一般化的分数阶事件触发控制器设计。该方法充分发挥前馈神经网络可以任意逼近平方可积函数的优势,对传统事件触发控制过程进行学习,有效解决了传统方法对具体数学模型的依赖问题,且可进一步降低事件触发控制的次数。另外,相较于传统的整数阶控制器,分数阶控制器拓宽了事件触发控制算法的实际应用范围。以具体应用为背景进行仿真分析,结果表明,该算法不但能够保持传统事件触发控制算法在节省控制资源方面的优势,而且在可移植性方面优于传统的事件触发控制算法。

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.