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A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock

APPLIED SOFT COMPUTING 2014年 19卷 86-92页

作者： Wei, Liang-Ying Cheng, Ching-Hsue Wu, Hsin-Hung Yuanpei Univ Dept Informat Management Hsinchu 30015 Taiwan Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu 640 Yunlin Taiwan Natl Changhua Univ Educ Dept Business Adm Changhua 500 Taiwan

Linear model is a general forecasting model and moving average technical index (MATI) is one of useful forecasting methods to predict the future stock prices in stock markets. Therefore, individual investors, stock fund managers, and financial analysts attempt to predict price fluctuation in stock markets by either linear model or MATI. From literatures, three major drawbacks are found in many existing forecasting models. First, forecasting rules mined from some AI algorithms, such as neural networks, could be very difficult to understand. Second, statistic assumptions about variables are required for time series to generate forecasting models, which are not easily understandable by stock investors. Third, stock market investors usually make short-term decisions based on recent price fluctuations, i.e., the last one or two periods, but most time series models use only the last period of stock price. In order to overcome these drawbacks, this study proposes a hybrid forecasting model using linear model and MATI to predict stock price trends with the following four steps: (1) test the lag period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and calculate the last n-period moving average;(2) use subtractive clustering to partition technical indicator values into linguistic values based on data discretization method objectively;(3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset, and optimize the FIS parameters by adaptive network;and (4) refine the proposed model by adaptive expectation models. The proposed model is then verified by root mean squared error (RMSE), and a ten-year period of TAIEX is selected as experiment datasets. The results show that the proposed model is superior to the other forecasting models, namely Chen's model and Yu's model in terms of RMSE. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Autocorrelation Subtractive clustering adaptive-network-based fuzzy inference system Moving average adaptive learning

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Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2015年第9期60卷 1566-1586页

作者： Kitsikoudis, Vasileios Sidiropoulos, Epaminondas Hrissanthou, Vlassios Democritus Univ Thrace Dept Civil Engn GR-67100 Xanthi Greece Aristotle Univ Thessaloniki Dept Rural & Surveying Engn GR-54006 Thessaloniki Greece

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches;however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

关键词： adaptive-network-based fuzzy inference system artificial neural networks genetic programming machine learning sand-bed rivers sediment transport

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ANFIS MODELS FOR THE QUASISTATIC ANALYSIS OF COPLANAR STRIP LINE STRUCTURES

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2010年第9期52卷 1990-1996页

作者： Turkmen, Mustafa Yildiz, Celal Guney, Kerim Kaya, Sabri Erciyes Univ Dept Elect & Elect Engn Fac Engn TR-38039 Kayseri Turkey

In this work, computer-aided design models based on adaptive-network-based fuzzy inference system (ANFIS) for the quasistatic analysis of three different coplanar strip line structures are presented. These strip line structures are conventional coplanar strip lines, asymmetrical coplanar strip lines with infinitely wide strip, and asymmetrical coplanar strip lines with infinitely thick dielectric substrate. The design parameters of the proposed ANFIS models are optimally determined by using four different optimization algorithms, hybrid learning, simulated annealing, least-squares, and genetic algorithm. When the performances of ANFIS models are compared with each other, the best results for training and test are obtained from the models trained with hybrid learning algorithm. There is a good agreement among the results of ANFIS models, quasistatic analysis, a full-wave. electromagnetic simulator IE3D, and experimental works realized in this study. (C) 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1990-1996,2010;Published online in Wiley InterScience (***). DOI 10.1002/mop.25372

关键词： coplanar strip lines effective permittivity characteristic impedance adaptive-network-based fuzzy inference system

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A shuffled frog-leaping algorithm based mixed-sensitivity H control of a seismically excited structural building using MR dampers

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JOURNAL OF VIBRATION AND CONTROL 2018年第13期24卷 2832-2852页

作者： Lin, Xiufang Chen, Shumei Huang, Guorong Fuzhou Univ Coll Mech Engn Fuzhou 350116 Fujian Peoples R China Fujian Agr & Forrest Univ Jianshan Coll Dept Informat & Mechatron Engn Fuzhou Fujian Peoples R China

An intelligent robust controller, which combines a shuffled frog-leaping algorithm (SFLA) and an H control strategy, is designed for a semi-active control system with magnetorheological (MR) dampers to reduce seismic responses of structures. Generally, the performance of mixed-sensitivity H (MSH) control highly depends on expert experience in selecting the parameters of the weighting functions. In this study, as a recently-developed heuristic approach, a multi-objective SFLA with constraints is adopted to search for the optimal weighting functions. In the proposed semi-active control, firstly, based on the Bouc-Wen model, the forward dynamic characteristics of the MR damper are investigated through a series of tensile and compression experiments. Secondly, the MR damper inverse model is developed with an adaptive-network-based fuzzy inference system (ANFIS) technique. Finally, the SFLA-optimized MSH control approach integrated with the ANFIS inverse model is used to suppress the structural vibration. The simulation results for a three-story building model equipped with an MR damper verify that the proposed semi-active control method outperforms fuzzy control and two passive control methods. Besides, with the proposed strategy, the changes in structural parameters and earthquake excitations can be satisfactorily dealt with.

关键词： Optimization shuffled frog-leaping algorithm H control mixed sensitivity MR dampers adaptive-network-based fuzzy inference system structural vibration semi-active control

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Hybrid ANFIS-ant colony based optimisation for quadrotor trajectory tracking control

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2020年第1期34卷 13-25页

作者： Selma, Boumediene Chouraqui, Samira Abouaissa, Hassane Univ Sci & Technol Oran USTOMB Dept Informat Oran 31000 Algeria Univ Artois EA 3926 Lab Genie Informat & Automat Artois LGI2A F-62400 Bethune France

This study proposes a robust, accurate and intelligent control for trajectory tracking of a three degree of freedom quadrotor unmanned aerial vehicle (UAV). The controller is based on adaptive-network-based fuzzy inference system (ANFIS) and ant colony optimisation (ACO) algorithm. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the reference trajectory of the quadrotor in 2-D vertical plane. The ACO algorithm provides an automatic adjustment of ANFIS parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed ANFIS controller tuned by ACO, it is compared with ANFIS and proportional-integral-derivative (PID) controllers. As expected, the hybrid ANFIS-ACO controller gives more satisfactory results than the other methods already developed in the same study.

关键词： UAV unmanned aerial vehicle intelligent control ANFIS adaptive-network-based fuzzy inference system ACO ant colony optimisation

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Crack identification using neuro-fuzzy-evolutionary technique

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KSME INTERNATIONAL JOURNAL 2002年第4期16卷 454-467页

作者： Shim, MB Suh, MW Sungkyunkwan Univ Sch Mech Engn Jangan Gu Suwon 440746 South Korea

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the locationanddepthofthecrack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neurofuzzy-evolutionary technique, that is, adaptive-network-based fuzzy inference system (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving single objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigen-frequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

关键词： crack identification neuro-fuzzy-evolutionary technique structure analysis adaptive-network-based fuzzy inference system inverse analysis method Continuous Evolutionary Algorithms

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Synthesis and Research of Neuro-fuzzy Observer of Clamping Force for Mobile Robot Automatic Control system 1

Synthesis and Research of Neuro-Fuzzy Observer of Clamping F...

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1st IEEE International Conference on Data Stream Mining and Processing (DSMP)

作者： Kondratenko, Yuriy P. Kozlov, Oleksiy V. Gerasin, Oleksandr S. Zaporozhets, Yuriy M. Cleveland State Univ 2121 Euclid Ave Cleveland OH 44115 USA Natl Univ Shipbldg 9 Geroiv Stalingrada Ave Mykolaiv Ukraine Petro Mohyla Black Sea State Univ 10 68th Desantnykiv st Mykolaiv Ukraine

ISBN: (纸本)9781509037360

This paper is dedicated to solving the problem of a fuzzy observer development for the clamping force automatic control system of a mobile robot with magnetically-operated drivers for moving on inclined and vertical ferromagnetic surfaces. An approach based on physical rules of field theory to determine the clamping forces of electromagnets is also considered. The authors propose to synthesize the clamping force observer using hybrid neuro-fuzzy systems based on experimental data. The analysis of computer simulation results in the form of static characteristics graphs of the force observer confirms the high accuracy of the developed neuro-fuzzy observer to the real processes.

关键词： mobile robot wheel-mover electromagnet clamping device automatic control system adaptive-network-based fuzzy inference system force observer

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A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2014年 39卷 1-11页

作者： Xiao, Yi Liu, John J. Hu, Yi Wang, Yingfeng Lai, Kin Keung Wang, Shouyang Cent China Normal Univ Sch Informat Management Wuhan 430079 Peoples R China City Univ Hong Kong Ctr Transport Trade & Financial Studies Kowloon Hong Kong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Hong Kong Peoples R China

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for yshort-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Air transport demand forecasting Singular spectrum analysis adaptive-network-based fuzzy inference system Particle swarm optimization

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A Rough-fuzzy Hybrid Approach on a Neuro-fuzzy Classifier for High Dimensional Data

A Rough-Fuzzy Hybrid Approach on a Neuro-Fuzzy Classifier fo...

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International Joint Conference on Neural networks (IJCNN)

作者： Lee, Chang Su Edith Cowan Univ Sch Comp Sci Mt Lawley WA 6050 Australia

ISBN: (纸本)9781424496365

A new Rough-Neuro-fuzzy (RNF) classifier is proposed in this paper for pattern classification scheme on high dimensional data as an extension of the previous work. The rough set theory is utilized to reduce the given knowledge into a compact form and to obtain a minimal set of decision rules. The proposed Rough-Neuro-fuzzy classifier is constructed based on the structure of ANFIS (adaptive-network-based fuzzy inference system), except its connections determined by the reduced data and the generated decision rules obtained by the rough sets-based approach. This provides the compact and minimal number of configurations for the network to adjust itself towards a faster learning. The learning scheme for the proposed approach is adopted from the one in ANFIS. The TS-type fuzzy inference model is employed to perform the decision making process. The proposed system is applied on a number of data sets for pattern classification tasks using 10-fold cross validation. The number of attributes is reduced significantly and the minimal rules are generated effectively by the rough set-based approach in the proposed system. Experimental results showed that results produced by the proposed rough-neuro-fuzzy classifier may be competitive compared to the previous work and the other existing approaches.

关键词： 10-fold cross validation ANFIS Accuracy adaptive systems Pattern classification Set theory TS-type fuzzy inference model Testing Training data adaptive-network-based fuzzy inference system data analysis decision making process decision rules fuzzy neural nets fuzzy reasoning fuzzy set theory high dimensional data learning learning (artificial intelligence) neuro-fuzzy classifier pattern classification pattern classification rough set theory rough set theory rough-fuzzy hybrid approach

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A Nonlinear Control Method based on ANFIS and Multiple Models for a Class of SISO Nonlinear systems and Its Application

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IEEE TRANSACTIONS ON NEURAL networkS 2011年第11期22卷 1783-1795页

作者： Zhang, Yajun Chai, Tianyou Wang, Hong Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Manchester Control Syst Ctr Manchester M60 1QD Lancs England

This paper presents a novel nonlinear control strategy for a class of uncertain single-input and single-output discrete-time nonlinear systems with unstable zero-dynamics. The proposed method combines adaptive-network-based fuzzy inference system (ANFIS) with multiple models, where a linear robust controller, an ANFIS-based nonlinear controller and a switching mechanism are integrated using multiple models technique. It has been shown that the linear controller can ensure the boundedness of the input and output signals and the nonlinear controller can improve the dynamic performance of the closed loop system. Moreover, it has also been shown that the use of the switching mechanism can simultaneously guarantee the closed loop stability and improve its performance. As a result, the controller has the following three outstanding features compared with existing control strategies. First, this method relaxes the assumption of commonly-used uniform boundedness on the unmodeled dynamics and thus enhances its applicability. Second, since ANFIS is used to estimate and compensate the effect caused by the unmodeled dynamics, the convergence rate of neural network learning has been increased. Third, a "one-to-one mapping" technique is adapted to guarantee the universal approximation property of ANFIS. The proposed controller is applied to a numerical example and a pulverizing process of an alumina sintering system, respectively, where its effectiveness has been justified.

关键词： adaptive-network-based fuzzy inference system multiple models nonlinear systems pulverizing system unstable zero dynamics

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