带并行机调度问题中基于ANFIS的自适应算法

针对用规则的线性组合方法解决实际的复杂调度中,如何确定哪些规则需要组合和确定权系数一直缺乏有效的方法,提出了根据多个不同的生产调度目标采用ANFIS将不同调度目标的规则进行自适应的综合,进行学习后产生模糊规则的算法,并将其用于带并行机Jobshop调度问题中。数值计算结果表明,该算法能够较好地对一些规则进行综合,并且在此类调度问题中有比这些规则更好的效果。今后的研究方向是调整ANFIS的结构使其更适合调度问题。     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems

In this paper implementation of ANFIS on embedded systems based on single-core and multi-core ARM processors is presented. A novel evolutionary optimization tool named, modified high performance genetic algorithm (mHPGA) with bacterial conjugation operator is applied to ANFIS as a training method. Fixed point and floating point number representations are applied and compared. Moreover new mutation algorithm has been proposed for fixed point numbers. The proposed method is designed to sweep numbers space to search possible solutions in large state space. Concurrency nature of mHPGA benefits implementation of multi threading feature on ARM cortex-A53 with four cores.     

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%.     

Soft computing methods applied to combination of one-class classifiers

The paper shows the possibilities of generalizing the two-class classification into multi-class classification by means of a fuzzy inference system. Fuzzy combiner harnesses the support values from classifiers to provide final response having no other restrictions on their structure. We compare proposed combination methods with ECOC and two variations of decision templates, based on Euclidean and symmetric distance. The effectiveness of the proposed combination method based on the fuzzy logic theory is also evaluated via computer experiments carried out on benchmark datasets.     

An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach

The aim of this study is to design a HVAC system which damper gap rates have been controlled by PID controller. One of the dampers was controlled by using the required temperature for the interested indoor volume while the other damper was controlled by using the required humidity for the same indoor volume. The realized system has a zone with variable flow-rate by considering the ambient temperature and humidity. In the authors’ previous theoretical work, PID parameters were theoretically obtained by using fuzzy sets for the same HVAC system. Optimization with Fuzzy Modeling Approach of PID parameters has been performed to maximize the performance of the system. The obtained PID parameters in the previous theoretical work were used in this study. Besides, the damper gap rates of a HVAC system with only one zone were predicted by using Artificial Neural Fuzzy Interface System (ANFIS) method. The input-output data sets of this system were first stored and then these data sets were used to obtain its intelligent model and control based on ANFIS. Efficiency of the developed ANFIS method was tested and a mean 99.98% recognition success was obtained. This paper shows that the values predicted with the ANFIS can be used to predict damper gap rate of HVAC system quite accurately. Therefore, faster and simpler solutions can be obtained based on ANFIS.     

An ANFIS-based approach for predicting the bed load for moderately sized rivers

A total of 346 sets of bed-load data obtained from the Kinta River, Pari River, Kerayong River and Langat River were analyzed using four common bed-load equations. These assessments, based on the median sediment size (d₅₀), show that the existing equations were unable to predict the measured bed load accurately. All existing equations over-predicted the measured values, and none of the existing bed-load equations gave satisfactory performance when tested on local river data. Therefore, the present study applies a new soft computing technique, i.e. an adaptive neuro-fuzzy inference system (ANFIS), to better predict measured bed-load data. Validation of the developed network (ANFIS) was performed using a new set of bed-load data collected at Kulim River. The results show that the recommended network can more accurately predict the measured bed-load data when compared to an equation based on a regression method.     

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.     

Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 3. Cutting Force Estimation Using Current Sensors

This paper presents a new method for measurement of cutting force using reliable and inexpensive current sensors. The relationship between the various factors which affect the performance of the spindle and feed drive systems are analysed, together with models of the spindle and feed drive system. The tangential (Ft) and axial cutting forces (Fa) are measured, using a neuro-fuzzy technique, with inexpensive current sensors installed on the a.c. servomotors of a CNC turning centre. The normal cutting pressure (Kn) and effective friction coefficient (Kf ) are calculated using the model of the cutting force and the two cutting forces measured by motor current, then the radial cutting force (Fr) can be calculated based on the model of cutting force. Experimental results show that the method can measure tangential, axial and radial cutting forces within errors of 10%, 5% and 25%, respectively, so the need for controlling or monitoring the cutting processes can be met in practice.