发酵过程的多模型融合建模算法

提出了利用多模型融合技术进行发酵过程建模的新方法， 该方法能够将在线参数和离线参数同时用于建模中． 首先给出了多模型融合建模算法框架， 并描述了基于自适应模糊神经网络和模糊推理技术两个参与融合的子模型的建立方法. 采用三个非线性函数分别运用GMDH-PTSV算法、傅里叶神经网络和多模型融合建模算法进行建模精度比较. 最后给出了多模型融合建模算法在青霉素发酵过程中应用的结果．     

基于模糊粗糙神经网络的发酵过程建模

对于发酵这样一个非线性的动态过程,由于其复杂性和在线传感器的缺乏,使得过程中的一些重要状态变量难以在线测量,从而给发酵过程的优化柠制带来了极大的困难,为此,结合模糊粗糙集和智能控制的理论,充分利用模糊粗糙集和神经网络两者的优点,提出了一种新型的网络-模糊粗糙神经网络实现对发酵过程的建模和状态估计,结果表明陵网络模型的结构简单,可解释性强。收敛速度快,能够较为准确地拟合过程的动态特性,预估能力较强。     

发酵过程的建模与优化方法研究

对于发酵这样一个复杂的非线性动态过程,由于在线传感器的缺乏,使得过程中的一些重要状态变量难以在线测量,从而给发酵过程的优化控制带来了极大困难。为此,提出了一种新型的动态网络—递归补偿模糊神经网络方法,实现对发酵过程的建模和状态估计,结果表明该网络能够较为准确地拟合过程的动态特性。进一步采用改进的蚁群算法来对发酵过程的控制变量进行优化,使发酵的产物产量得到提高。该方法应用于多粘菌素的发酵生产过程中,实现了状态变量的在线预估与控制变量的在线优化。     

青霉素发酵过程建模研究

青霉素发酵过程是一种具有非线性、时变性的复杂生化反应系统,由于一些生物参数在线检测困难,许多生化过程的代谢途径尚不明确,难以建立精确数学模型.而神经网络具有非线性、多变量、自学习、并行处理等特点,用于非线性系统的建模具有无可比拟的优势.因此,以青霉素发酵过程生化机理模型产生的数据为样本,训练RBF神经网络,建立了基于RBF神经网络的发酵过程模型.该模型可用于发酵过程中状态变量的估算与预测,并且可估计底物、产物、菌体浓度的变化趋势,对实际工作具有指导意义.     

花生四烯酸发酵过程的建模方法研究

对发酵法生产花生四烯酸的建模方法进行了初步研究,并基于四种温度下的试验数据,建立了发酵过程模型.比较分析了最小二乘支持向量机( LS-SVM)和广义回归神经网络(GRNN)这两种方法的特点.结果表明,这两种方法均能较好地建立该发酵过程的模型,LS-SVM建模的预测能力稍优于GRNN,为后续花生四烯酸发酵过程的优化及控制的研究奠定了基础.     

基于变量选取的ANFIS技术及在发酵过程中的建模

提出了一种基于主成分分析选取变量的自适应模糊神经网络的建模方法,将该方法与其他三种未经变量选取的苔适应模糊神经网络的方法的建模精度进行了比较,结果表明该方法的建模精度较好。最后采用真实青霉素发酵过程数据进行模型验证．仿真结果表明该方法具有较好的建模精度和实用性。     

基于SSH框架神经网络建模在发酵过程中的应用

发酵过程中的一些关键生物参数难以实时在线测量,融合人工智能技术和面向对象的编程技术,设计并实现了一种基于Web的建模系统,集成各种神经网络及其优化算法对生物参数进行建模预估.利用Eclipse的开发平台,使用轻量缓框架,分离了表示层、业务逻辑层和数据持久层,在业务逻辑层对神经网络算法进行了实现,并完成了Java与Matlab的交互.该系统可用于实际的软测量建模,还可用于对不同建模方法的分析、比较.对赖氨酸发酵过程的实际应用效果验证了软件的有效性和可行性.     

自适应神经模糊推理系统建模研究

With rapid development of the fuzzy control application field, the existing system for fuzzy inferring modeling cannot more and more suit the requirements of fuzzy control. About how to apply the theories of fuzzy control to practice rapidly and conveniently, this paper presents a reasonable and practical method, which supports all sorts of fuzzy inferring system of MAMDANI and SUGENO to be modeled not only by tuning references of membership functions, but also by tuning fuzzy inferring structure. The modeling instance shows that it's practical and effective.     

改进型Elman神经网络发酵过程建模研究

依据发酵过程的机理和改进的Elman神经网络动态建模原理,提出了一个新的发酵过程建模分批训练算法。通过发酵过程仿真实验,与传统的BP建模算法比较,改进的Elman神经网络建模算法具有收敛速度快、泛化能力强等特点。此外,利用该算法编制的软件可以内嵌到发酵过程监控系统中,实现发酵过程在线建模与状态参量的在线预估。     

基于ANFIS的非线性电机系统的建模

将一种神经—模糊结构—自适应神经模糊推理系统 (简称ANFIS)用于非线性电机系统的建模 ,获得了一个良好的大范围的全局非线性模型 ,同时 ,通过与反向传播网络建模结果的性能对比 ,说明ANFIS在参数收敛速度及建模精度上的优越性。显示出ANFIS是非线性系统的建模、辨识的有力工具     

Application of an adaptive neural fuzzy inference system to thermal comfort and group technology problems

The Adaptive Neural Fuzzy Inference System (ANFIS) is used to design two vague systems, namely thermal comfort and group technologies in production and operations management. Results show that both systems can be modeled successfully by the combined use of a fuzzy approach and neural network learning.     

Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

The purpose of this article is to demonstrate the use of feedforward neural networks (FFNNs), adaptive neural fuzzy inference systems (ANFIS), and probabilistic neural networks (PNNs) to discriminate between earthquakes and quarry blasts in Istanbul and vicinity (the Marmara region). The tectonically active Marmara region is affected by the Thrace-Eski?ehir fault zone and especially the North Anatolian fault zone (NAFZ). Local MARNET stations, which were established in 1976 and are operated by the Kandilli Observatory and Earthquake Research Institute (KOERI), record not only earthquakes that occur in the region, but also quarry blasts. There are a few quarry-blasting areas in the Gaziosmanpa?a, Çatalca, Ömerli, and Hereke regions. Analytical methods were applied to a set of 175 seismic events (2001-2004) recorded by the stations of the local seismic network (ISK, HRT, and CTT stations) operated by the KOERI National Earthquake Monitoring Center (NEMC). Out of a total of 175 records, 148 are related to quarry blasts and 27 to earthquakes. The data sets were divided into training and testing sets for each region. In all the models developed, the input vectors consist of the peak amplitude ratio (S/P ratio) and the complexity value, and the output is a determination of either earthquake or quarry blast. The success of the developed models on regional test data varies between 97.67% and 100%.     

A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.     

Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques

The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated.The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period.     

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.     

直升机旋翼自转着陆过程的仿真模型

该文提出了一种新的复杂大系统建模方法。采用模糊神经网络对复杂大系统对象中的结构不确定性因素进行建模,并利用同步扰动随机近似算法对模型参数进行在线调整。应用该方法建立了直升机旋翼自转着陆过程的仿真模型,给出了全量模型和模型调整过程。与传统的机理模型相比,该模型具有结构简单、并行解算程度高、运算速度快的特点,适合于实时仿真环境。仿真结果和工程应用表明,文中所提建模方法是切实可行的。