Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina–water nanofluids

By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network as well as experimental data, two models were established in order to predict the thermal conductivity ratio of alumina (Al₂O₃)–water nanofluids. In these models, the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al₂O₃ nanoparticle size were considered as the input (design) parameters. The empirical data were divided into train and test sections for developing the models. Therefore, they were instructed by 80% of the experimental data and the remaining data (20%) were considered for benchmarking. The results, which were obtained by the proposed FCM-based neuro-fuzzy inference system (FCM-ANFIS) and genetic algorithm-polynomial neural network (GA-PNN) models, were provided and discussed in detail.     

Proton exchange membrane fuel cell performance prediction using artificial neural network

Polarization curves remain one of the parameters used to check the performance of fuels in terms of efficiency and durability. This investigation explores the application of artificial neutral network (ANN) to determine the voltage and current from a proton exchange membrane fuel cell having membrane area of 11.46 cm². Performance predictability for the group method of data handling (GMDH) as well as feed forward back propagation (FFBP) neutral networks were employed for the estimation of the current and voltage obtained from the Proton Exchange Membrane Fuel cell under investigation. The investigation presented models with good predictions even though GMDH neural network performed better than the FFBP neural network. The study therefore proposes the GMDH neural network as the best model for predicting the performance of a Proton Exchange Membrane Fuel cell. It was further deduced that an increase in reactant flow rate has direct effect on the performance of the fuel cell but this is directly proportional to the total irreversibilities in the cell hence to operate fuel cell economically, it is imperative that the hydrogen flow is made lower compare to the oxygen flow rate. This in effect will reduce the pumping power required for the flow of the fuel hence reducing the net loss in the cell.     

基于最优组合的一类非线性建模方法及其在预测中的应用

１引言专门人才预测大系统不仅涉及社会、经济、科技、教育等诸方面,而且受地理、历史背景和民族特点的约束．因此,如何利用现有条件定量地预测未来专门人才的需求仍是一个极待解决的问题．２最优组合准则及其探讨用基本数据处理组合法（ＧｒｏｕｐＭｅｔｈｏｄｏｆＤａ...     

A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing

Nowadays Network function virtualization (NFV) has drawn immense attention from many cloud providers because of its benefits. NFV enables networks to virtualize node functions such as firewalls, load balancers, and WAN accelerators, conventionally running on dedicated hardware, and instead implements them as virtual software components on standard servers, switches, and storages. In order to provide NFV resources and meet Service Level Agreement (SLA) conditions, minimize energy consumption and utilize physical resources efficiently, resource allocation in the cloud is an essential task. Since network traffic is changing rapidly, an optimized resource allocation strategy should consider resource auto-scaling property for NFV services. In order to scale cloud resources, we should forecast the NFV workload. Existing forecasting methods are providing poor results for highly volatile and fluctuating time series such as cloud workloads. Therefore, we propose a novel hybrid wavelet time series decomposer and GMDH-ELM ensemble method named Wavelet-GMDH-ELM (WGE) for NFV workload forecasting which predicts and ensembles workload in different time-frequency scales. We evaluate the WGE model with three real cloud workload traces to verify its prediction accuracy and compare it with state of the art methods. The results show the proposed method provides better average prediction accuracy. Especially it improves Mean Absolute Percentage Error (MAPE) at least 8% compared to the rival forecasting methods such as support vector regression (SVR) and Long short term memory (LSTM).     

基于GMDH建模的蒸汽发生器水位重构

蒸汽发生器水位指示仪表出现虚假指示或丧失指示的情况时有发生,而目前又没有很好的方法实现蒸汽发生器水位的重新标定,主要靠经验来进行判断,所以当事故或故障发生时严重影响操纵员对核动力装置运行情况的判断。自组织理论模型（GMDH）是建立复杂非线性大系统数学模型十分灵活而通用的方法,在处理复杂非线性对象中能得到很好的效果。本文以主蒸汽管道破口事故下重构蒸汽发生器水位为例,提出了用GMDH重构蒸汽发生器水位的方法,并与仿真结果进行对比。结果表明,GMDH对蒸汽发生器水位重构的相对误差小、精度高,满足实际需要,能为船用核动力装置的安全运行做出指导。     

GMDH-PTSV 算法及其在发酵过程建模中的应用

本文提出了GMDH-PTSV(具有主成份双向变量选择器的GMDH)建模算法。在此算法中,变量的选择是根据对系统的测量数据,而不是根据人的经验构造出的(0-1)相关矩阵。通过仿真,对GMDH-PTSV算法和其它三种算法进行了比较。最后,应用GMDH-PTSV算法对一种生物发酵过程建立了模型。     

基于GMDH的组合预测模型应用研究

应用数据处理的分组方法（GMDH）多层算法、GMDH自回归算法、多维AC算法、单维AC算法，建立了基于GMDH的工业增加值预测模型，在此基础上建立了最优线性组合预测模型。实验证明本文方法不仅改善了模型对数据样本的拟合精度，而且提高了模型的预测能力。     

基于混沌优化GMDH网络的月降水量预测

为提高降水量预测的精确度,介绍了一种基于混沌优化的GMDH网络预测方法,该方法利用混沌优化算法全局搜索GMDH网络的初始权值,并利用优化后的GMDH网络建立预测模型对月降水量进行预测。结果表明:该方法能加快GMDH网络结构稳定的速度,使算法快速收敛到全局最优解,对月降水量的动态预报具有一定的实用价值。     

The k-nearest neighbour-based GMDH prediction model and its applications

This paper centres on a new GMDH (group method of data handling) algorithm based on the k-nearest neighbour (k-NN) method. Instead of the transfer function that has been used in traditional GMDH, the k-NN kernel function is adopted in the proposed GMDH to characterise relationships between the input and output variables. The proposed method combines the advantages of the k-nearest neighbour (k-NN) algorithm and GMDH algorithm, and thus improves the predictive capability of the GMDH algorithm. It has been proved that when the bandwidth of the kernel is less than a certain constant C, the predictive capability of the new model is superior to that of the traditional one. As an illustration, it is shown that the new method can accurately forecast consumer price index (CPI).     

Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks

We propose a new category of neurofuzzy networks—self-organizing neural networks (SONN) with fuzzy polynomial neurons (FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are discussed. Each of them comes with two topologies such as a generic and advanced type. Especially in the advanced type, the number of nodes in each layer of the SONN architecture can be modified with new nodes added, if necessary. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. The architecture of the SONN is not fixed in advance as it usually takes place in the case of conventional neural networks, but becomes organized dynamically through a growth process. Simulation involves a series of synthetic as well as real-world data used across various neurofuzzy systems. A comparative analysis shows that the proposed SONN are models exhibiting higher accuracy than some other fuzzy models.