A robust prediction model using ANFIS based on recent TETRA outdoor RF measurements conducted in Riyadh city – Saudi Arabia

Received wireless signal prediction is a difficult and complex task. Various types of prediction models such as deterministic, empirical, as well as statistic were developed. However, they rarely adapt well to different types of environments. Prediction models based on artificial intelligence techniques are the recent alternative approaches to predict the signal strength at a particular location in an investigated area. The advantage of using artificial intelligence for field strength prediction is given by the flexibility to adapt to different environments, high-speed processing, and the ability to process a high quantity of data. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used as a robust wireless signal predictor. The performance of the proposed predictor is then compared to the predictors based on radial basis function neural network (RBF-NN), and three most widely used empirical path loss models. The performance criterion selected for the comparison between the actual and the predicted data are the root mean square error (RMSE), maximum relative error (MRE), and goodness of fit (R²). It turns out that the ANFIS prediction model outperforms the predictors based on empirical models, and is marginally better than RBF-NN predictor.     

Learning and generalization of noisy mappings using a modifiedPROBART neural network

Incremental function approximation using the PROBART neural network offers many advantages over conventional feedforward networks. These include dynamic node allocation based on the complexity of the function approximation task, guaranteed convergence, and the ability to handle noise in the training data. However, the PROBART network does not generalize very well to untrained data. In this paper, a modified PROBART is proposed to overcome this deficiency. This modification replaces the winner-take-all mode of prediction of the PROBART with a distributed mode of prediction. This distributed mode enables several neurons to cooperate during prediction and, thus, provides better generalization capabilities even in noisy conditions. Computer simulations are conducted to evaluate the performance of the modified PROBART neural network using three benchmark nonlinear function approximation tasks. The prediction accuracy of the modified PROBART network compares favorably to the PROBART, fuzzy ARTMAP, and ART-EMAP networks for all these tasks     

A Plasma‐Etching Process Modeling Via a Polynomial Neural Network

A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma‐etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high‐prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.     

基于BP神经网络的紫外光源自适应固化控制研究

为解决紫外光动态固化技术中的固化不充分或反固化反应等问题,提出一种基于BP算法的LED紫外光源多参数自适应控制方法.利用神经网络优异的非线性逼近能力,并结合优化后的BP算法构建一个3输入2输出的网络预测模型.通过与多元线性回归和多元非线性回归模型的对比显示,BP神经网络算法有更高的拟合度.最后将57组数据导入训练好的模...     

Network traffic prediction based on INGARCH model

In this paper, we introduce the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is known as a non-linear analytical model that could capture the characteristics of network traffic such as Poisson packet arrival and long-range dependence property, INGARCH seems to be an adequate model for network traffic prediction. Based on the investigation for the traffic arrival process in various network topologies including IoT and VANET, we could confirm that assuming the Poisson process as packet arrival works for some networks and environments of networks. The prediction model is generated by estimating parameters of the INGARCH process and predicting the Poisson parameters of future-steps ahead process using the conditional maximum likelihood estimation method and prediction procedure, respectively. Its performance is compared with those of three different models; autoregressive integrated moving average, GARCH, and long short-term memory recurrent neural network. Anonymized passive traffic traces provided by the Center for Applied Internet Data Analysis are used in the experiment. Numerical results show that the proposed model predicts better than the three models in terms of measurements used in prediction models. Based on the study, we can conclude the followings: INGARCH can capture the characteristics of network traffic better than other statistic models, it is more tractable than neural networks (NNs) overcoming the black-box nature of NNs, and the performances of some statistical models are comparable or even superior to those of NNs, especially when the data is insufficient to apply deep NNs.

     

Universal approximation capability of EBF neural networks with arbitrary activation functions

The purpose of this paper is to investigate the approximation capability of elliptic basis function (EBF) neural networks. The main results are: (1) A necessary and sufficient condition for a functionS (R ¹) to be qualified as an activation function in the hidden layer of an EBF neural network is given. (2) Every nonzero functionG L ²(R ⁿ ) is qualified to be an activation function in the elliptic neural network to approximate any function in L²(Rⁿ). (3) As applications, we give new proofs of the theorems concerning the approximation capability of affine basis function (ABF) neural networks and generalized radial basis function (GRBF) neural networks (including radial basis function neural networks) with arbitrary activation functions. In particular, we solve the problem of the approximation capability of sigma-pi neural networks.Work was supported in part by CNSF, the Shanghai Science Foundations and Doctoral Program of Education Commissions of China.     

基于神经网络的网络流量预测算法研究

网络流量预测有助于网络服务质量的提升和网络资源的合理分配,对优化网络管理与运营、保障用户体验质量至关重要.因特网业务的急剧增加和基础网络的快速发展导致网络流量变得更加复杂多样,传统网络流量预测模型难以保证较高的预测精度,而神经网络作为人工智能的重要分支,在预测复杂网络流量时具有显著优势.简述反向传播神经网络、径向基神经...     

Privacy Based Data Publishing Model for Cloud Computing Environment

The evaluation of corporate social responsibility (CSR) performance may enhance companies’ willingness to undertake social responsibilities, so it is very important to improve the quality of CSR performance evaluation. Based on the three factors of economic performance, social performance and environmental performance, this paper proposed an improved analytic hierarchy process-back propagation (AHP-BP) neural network algorithm, and introduced the improved AHP-BP neural network algorithm into CSR performance evaluation model. In the stage of improved AHP, the model included the importance of the knowledge and experience of the experts by expert scoring, and reduced the subjective influence of expert judgment on the results by introducing a personality test scale. In the stage of BP neural network, trained models have been used for CSR performance evaluation. The results showed that the prediction result of improved AHP-BP neural network model was better than that of BP neural network model. Therefore, the improved AHP-BP neural network algorithm can be used as a good predictor for CSR performance evaluation.

     

Adaptive neuro-fuzzy inference system for gap discontinuities in coplanar waveguides

A new approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for gap discontinuities in coplanar waveguides (CPWs). The proposed ANFIS model combines the neural network adaptive capabilities and the fuzzy qualitative approach. The ANFIS is presented so as to produce a good approximation to the nonlinear relationship between the geometrical parameters and the frequency-dependent equivalent circuit parameter (the S ₂₁ parameter of the gap). The ANFIS results for the S ₂₁ parameters are compared with the results available in the literature obtained by using the conformal-mapping technique (CMT), and the results confirm that the proposed ANFIS model can provide an accurate computation of the S ₂₁ parameters of the gaps in CPWs.     

Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68?×?10^?5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.     

Neural networks for modeling nonlinear memoryless communicationchannels

This paper presents a neural network approach for modeling nonlinear memoryless communication channels. In particular, the paper studies the approximation of the nonlinear characteristics of traveling-wave tube (TWT) amplifiers used in satellite communications. The modeling is based upon multilayer neural networks, trained by the odd and even backpropagation (BP) algorithms. Simulation results demonstrate that neural network models fit the experimental data better than classical analytical TWT models,     

Comparing models for the growth of silicon-rich oxides (SRO)

The relative advantages of several methods for modeling the growth of Silicon-Rich Oxide (SRO) films are compared. The methods are a response surface model, a physical model based on chemical kinetics, and neural network models. The physical model provides more insight and greater predictive ability. Neural network models provide better fits to complex response surfaces with minimal data and can be used successfully in the absence of a theoretical model. The risks of prediction by neural networks outside their training domain are demonstrated     

ARMA建模在神经网络卡钻预测方法中的应用研究

为准确预测卡钻事故的发生,利用一种基于时间序列的神经网络卡钻预测方法,将时间序列ARMA建模与神经网络非线性建模相结合。选取与卡钻事故相关性较大的参数作为神经网络的输入项,运用现场数据对神经网络进行训练,再利用神经网路的强非线性和自适应学习能力来建立卡钻事故预测模型;通过时间序列对历史数据的挖掘功能,揭示实际钻井过程中对卡钻事故影响较大的各参数的隐含规律,建立时序ARMA模型,求出卡钻时刻钻井相关参数的预测值;将预测值放入神经网络模型进行测试训练,从而达到预测卡钻事故的效果。运用延安地区实际现场数据证实该方法具有精确的卡钻预测能力及较好的泛化能力。     

小波神经网络在短时交通流量预测中的应用

随着交通基础设施建设和智能运输系统的发展,交通规划和交通诱导成为交通领域的研究热点,对交通规划和交通诱导而言,准确的交通流量预测是其实现的前提和关键。短时交通流量预测是一个时间序列预测问题,文中应用小波神经网络对短时交通流量进行了预测。首先,对神经网络、小波分析等相关理论进行了简要介绍。在此基础上,采用5-7-1小波神经网络结构,以Morlet小波基函数作为隐含层节点的传递函数,将车流量数据输入该模型中,以训练小波神经网络,并用训练好的神经网络来预测短时交通流量。从预测结果来看,小波神经网络的预测结果较为准确,网络预测值接近期望值,效果较好。     

Comparison of adaptive-network-based fuzzy inference system models for resonant frequency computation of circular microstrip antennas

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared to the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. Published in Russian in Radiotekhnika i Elektronika, 2009, Vol. 54, No. 4, pp. 389–400. The article is published in the original.     

高光谱成像技术检测冷却羊肉表面细菌总数

为了对冷却羊肉表面细菌总数进行无损检测,采用不同波段范围高光谱成像系统结合多种建模方法建立预测模型,进行理论分析和实验验证。分别在400nm~110nm和900nm~1700nm波长范围内获取冷却羊肉样本的高光谱图像信息,结合偏最小二乘和人工神经网络(反向人工神经网络和径向基人工神经网络)建立预测模型。结果表明,神经网络建模效果优于偏最小二乘;其中,径向基人工神经网络模型在400nm~1100nm和900nm~1700nm波长范围内相关系数分别为0.9872和0.9988,均方根误差分别为0.8210和0.2507,预测效果最好;而900nm~1700nm波长范围为最佳建模波长。这一结果说明利用高光谱图像技术对冷却羊肉表面细菌总数进行快速无损检测是可行的。     

Environment-adaptation mobile radio propagation prediction usingradial basis function neural networks

This paper investigates the application of a radial basis function (RBF) neural network to the prediction of field strength based on topographical and morphographical data. The RBF neural network is a two-layer localized receptive field network whose output nodes from a combination of radial activation functions computed by the hidden layer nodes. Appropriate centers and connection weights in the RBF network lead to a network that is capable of forming the best approximation to any continuous nonlinear mapping up to an arbitrary resolution. Such an approximation introduces best nonlinear approximation capability into the prediction model in order to accurately predict propagation loss over an arbitrary environment based on adaptive learning from measurement data. The adaptive learning employs hybrid competitive and recursive least squares algorithms. The unsupervised competitive algorithm adjusts the centers while the recursive least squares (RLS) algorithm estimates the connection weights. Because these two learning rules are both linear, rapid convergence is guaranteed. This hybrid algorithm significantly enhances the real-time or adaptive capability of the RBF-based prediction model. The applications to Okumura's (1968) data are included to demonstrate the effectiveness of the RBF neural network approach     

射频功率放大器的建模

提出了运用模糊神经网络对射频功放电路进行建模的方法,模糊神经网络是近年来发展起来的一种新型的网络结构,具有万能函数逼近器的功能,文中用MATALAB中自带的自适应神经模糊系统ANFIS对仿真得到的数据进行建模,并利用得到的模型计算功放的频谱,功率压缩曲线,功率增益曲线,与ADS仿真的结果进行比较,取得了较好的结果,证明了建模方法的有效性.     

Improved multipath routing with WNN for the open networks

Multipath routing mechanism is vital for reliable packet delivery, load balance, and flexibility in the open network because its topology is dynamic and the nodes have limited capability. This article proposes a new multipath switch approach based on traffic prediction according to some characteristics of open networks. We use wavelet neural network (WNN) to predict the node traffic because the method has not only good approximation property of wavelet, but also self-learning adaptive quality of neural network. When the traffic prediction indicates that the primary path is a failure, the alternate path will be occupied promptly according to the switch strategy, which can save time for the switch in advance. The simulation results show that the presented traffic prediction model has better prediction accuracy; and the approach based on the above model can balance network load, prolong network lifetime, and decrease the overall energy consumption of the network.     

Photovoltaic module simulation by neural networks using solar spectral distribution

A novel methodology based on artificial neural networks is proposed as an alternative to algebraic and numerical procedures to determine the I‐V curve of a module under different conditions. Although there are methods that use neural networks for approximating the I‐V curve, this is the first time that the measurement of the spectrum is incorporated as an input. In addition, a suitable selection of the training samples used to build the model is fundamental in order to get an accurate approximation. This is why a training sample selection based on a Kohonen self‐organizing map is performed in this paper instead of a random selection. With the use of this preliminary step, the performance of the network trained with spectral information improves over the one without spectral information. Copyright © 2012 John Wiley & Sons, Ltd.