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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Hassan, Saima Khanesar, Mojtaba Ahmadieh Jaafar, Jafreezal Khosravi, Abbas Univ Teknol PETRONAS Dept Comp & Informat Sci Perak Malaysia Kohat Univ Sci & Technol Inst Informat Technol Kohat Pakistan Semnan Univ Fac Elect & Comp Engn Semnan Iran Deakin Univ CISR Bldg KC Geelong Vic Australia

ISBN: (纸本)9781509018970

The major challenge in the design of Interval type 2 fuzzy logic system (IT2FLS) is to determine the optimal parameters for their antecedent and consequent parts. The most frequently used objective function for the design of IT2FLSs is root mean squared error (RMSE). However, other than RMSE, the maximum absolute error (MAE) for each of identification samples is very important. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed algorithm benefits from the combination of extreme learning machine (ELM) and non-dominated sorting genetic algorithm (NSGAII) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. The proposed method is used for forecasting of nonlinear dynamic systems. It is shown that not only the proposed method results in low RMSE, MAE achieved is also satisfactory.

关键词： Terms Interval type-2 fuzzy logic system extreme learning machine hybrid learning algorithm NSGAII

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Simulation of torsional shear test results with neuro-fuzzy control system

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2009年第2期29卷 253-260页

作者： Altun, S. Goektepe, A. B. Ansal, A. M. Akguener, C. Ege Univ Dept Civil Engn TR-35100 Izmir Turkey Kolin Const Co Akkoy Dam & HEPP Const Kurtun Gumushane Turkey Bogazici Univ Kandilli Observ Istanbul Turkey Bogazici Univ Earthquake Res Inst Istanbul Turkey Yildiz Tech Univ Dept Civil Engn TR-34349 Istanbul Turkey

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1 Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear stress-strain changes, the number of cycles to reach liquefaction and pore pressure variations were recorded. Results indicate that the liquefaction resistances of uniform sands are significantly affected by the method of sample preparation and initial conditions. In the second phase, an adaptive neuro-fuzzy inference methodology was applied for the simulation of nonlinear mapping among radial stress conditions, relative density, and number of load repetitions necessary for the liquefaction. The neuro-fuzzy model included a fuzzy approximate reasoning through a Sugeno fuzzy inference system (FIS) and the input space was fuzzified by a grid-partitioning technique. A hybrid-learning algorithm was selected for the adaptation of model parameters. Furthermore, linear-nonlinear regression analyses and neural network models were employed to observe relative performances. It is concluded based on the findings that the neuro-fuzzy control system exhibited a superior performance as compared to other employed methods. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Cyclic test Sand Liquefaction Neuro-fuzzy system hybrid learning algorithm

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Multitarget prediction using an aim-object-based asymmetric neuro-fuzzy system: A novel approach

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NEUROCOMPUTING 2020年 389卷 155-169页

作者： Tu, Chia-Hao Li, Chunshien Natl Cent Univ Dept Informat Management Lab Intelligent Syst & Applicat Taoyuan Taiwan

This paper proposes an aim-object-based asymmetric neuro-fuzzy system that is different from conventional models in two ways. First, this system has an asymmetric structure with different numbers of neurons in the premise and consequent layers. Secondly, with the assistance of the sphere complex fuzzy set, depending on the application, our model can alter the number of outputs. In addition, a hybrid learning algorithm combining the whale optimization algorithm and the recursive least-square estimator is proposed to optimize the proposed model. The results of the experiment show that the proposed model can simultaneously predict multiple targets with fewer parameters and maintain a performance level similar to that of the conventional neuro-fuzzy system. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Aim-object-based asymmetric Neuro-fuzzy system Aim-object layer Sphere complex fuzzy set Multitarget prediction hybrid learning algorithm

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A fast learnt fuzzy neural network for huge scale discrete data function approximation and prediction

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第4期24卷 693-701页

作者： Khayat, Omid Razjouyan, Javad Rahatabad, Fereidoun Nowshiravan Nejad, Hadi Chahkandi Islamic Azad Univ Young Researchers Club South Tehran Branch Tehran Iran Islamic Azad Univ Garmsar Branch Garmsar Iran Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Birjand Branch Birjand Iran

In realworld dataset, there are often large amount of discrete data that the concern is the interpolation and/or extrapolation by an approximation tool. Therefore, a training process will be actually used for definition and construction of the approximator parameters. Huge amount of data may lead to high computation time and a time consuming training process. To this concern a fast learnt fuzzy neural network as a robust function approximator and predictor is proposed in this paper. The learning procedure and the structure of the network is described in detail. Simplicity and fast learning process are the main features of the proposing Self-Organizing Fuzzy Neural Network (SOFNN), which automates structure and parameter identification simultaneously based on input-target samples. First, without need of clustering, the initial structure of the network with the specified number of rules is established, and then a training process based on the error of other training samples is applied to obtain a more precision model. After the network structure is identified, an optimization process based on the known error criteria is performed to optimize the obtained parameter set of the premise parts and the consequent parts. At the end, comprehensive comparisons are made with other approaches to demonstrate that the proposed algorithm is superior in term of compact structure, convergence speed, memory usage and learning efficiency.

关键词： Self-organizing fuzzy neural network hybrid learning algorithm function approximation prediction chaotic time series

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A New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System

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IETE JOURNAL OF RESEARCH 2011年第3期57卷 201-206页

作者： Sahu, Om Prakash Kumar, Sanjay Natl Inst Technol Kurukshetra Haryana India DRDO Terminal Ballist Res Lab Sect 30 Chandigarh India

The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on adaptive neuro-fuzzy inference system is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity.

关键词： Channel equalizer hybrid learning algorithm Intersymbol interference Membership function Optimal bayesian equalizer

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Training qubit neural network with hybrid genetic algorithm and gradient descent for indirect adaptive controller design

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 65卷 346-360页

作者： Ganjefar, Soheil Tofighi, Morteza Bu Ali Sina Univ Fac Engn Dept Elect Engn Shahid Fahmideh StPOB 65178-38683 Hamadan Iran

Heuristic stochastic optimization techniques such as genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic techniques such as gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution. The HGAGD is then used to train a qubit neural network (QNN), which is a good candidate for enhancing the computational efficiency of conventional neural networks, for two different applications. Firstly, a benchmark function is chosen to illustrate the potential of the proposed approach in dealing with function approximation problem. Subsequently, the feasibility of the proposed method in designing an indirect adaptive controller for damping of low frequency oscillations in power systems is studied. The results of these studies show that the proposed controller trained by the HGAGD can achieve satisfactory control performance. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： hybrid learning algorithm Memetic algorithm Qubit neural networks Power system controller Indirect adaptive control

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Sequential combination of statistics, econometrics and Adaptive Neural-Fuzzy Interface for stock market prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第7期37卷 5116-5125页

作者： Ansari, Tanvir Kumar, Manoj Shukla, Anupam Dhar, Joydip Tiwari, Ritu IIITM ABV Gwalior 474010 Madhya Pradesh India

Since last decade advanced data simulations help to identify hidden trends in a time series. Our purpose is to identify uncertainties during recession period using statistical analysis, econometrical analysis and Adaptive Neural-Fuzzy networks. In this paper, initially through computational analysis we are testing financial data using correlation tests, likelihood tests, heteroscedastic characteristics analysis and hypothesis tests. These statistical and econometrical tests give us exact nature of data set and relation between data points. All tests and analysis are studied on NASDAQ Stock Market over last 2-years. Then after, optimized subtractive data clustering method is used to cluster the data and create fuzzy membership functions by using Sugeno-type Fuzzy Interface System (FIS). Finally, we are using optimized hybrid learning algorithm in customized Adaptive Neural Fuzzy Interface System (ANFIS) to train the network. Hence, we got an efficient Adaptive Neural-Fuzzy network to check and test the data sets and use it for forecasting the stock market index. During this, the hybrid learning algorithm combines Least-Square method and the Back-propagation gradient descent methods for training the Fuzzy Interface System (FIS) with the help of optimized membership functions and parameters. This paper presents a state-of-art for Adaptive Neural-Fuzzy Network (ANFN) application to forecast stock market index and involved market uncertainties by combining the econometrical test to optimize the ANFIS and FIS function. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： GARCH/GJR/EGARCH (P, Q) hybrid learning algorithm Sugeno-type FIS Subtractive clustering Adaptive Neuro-Fuzzy Interface System

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Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting

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NATURAL HAZARDS 2021年第1期108卷 519-566页

作者： Tabbussum, Ruhhee Dar, Abdul Qayoom Natl Inst Technol Srinagar Dept Civil Engn Srinagar 190006 Jammu & Kashmir India

The ability of the adaptive neuro-fuzzy inference algorithm architecture to simulate floods is explored in this research. The development of models for flood forecasting has been centered on two adaptive neuro-fuzzy inference (ANFIS) algorithms. The Takagi-Sugeno fuzzy inference systems (FIS) generated through subtracted clustering were trained using hybrid and backpropagation training algorithms. Multiple statistical performance evaluators were used to assess the performability of the established models. The validity and predictive power of the models are evaluated by estimating a flood occurrence in the study area. In designing the models, a total of 12 inputs were employed. The best performability was found for the ANFIS model created utilizing a hybrid training algorithm with mean square error (MSE) of 0.00034, co-efficient of correlation (R-2) of 97.066%, root mean square error (RMSE) of 0.018, Nash-Sutcliffe model efficiency (NSE) of 0.968, mean absolute error (MAE) of 0.0073 and combined accuracy (CA) of 0.018, indicating the possible usage of exploiting the established model for prediction of floods.

关键词： Adaptive neuro-fuzzy inference system Backpropagation learning algorithm Gaussian membership function hybrid learning algorithm Subtractive clustering Takagi– Sugeno fuzzy inference system

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Evaluation of Ground Vibration Due to Blasting Using Fuzzy Logic

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2017年第3期35卷 1231-1237页

作者： Xue, Xinhua Yang, Xingguo Li, Peng Sichuan Univ State Key Lab Hydraul & Mt River Engn Coll Water Resource & Hydropower 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China Dadu River Hydropower Dev Co Ltd Chengdu 610041 Sichuan Peoples R China

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the evaluation of ground vibration due to blasting. The training of ANFIS was performed by a hybrid method of gradient descent method and least square algorithm. The least square algorithm is used to optimize the consequent parameters with the premise parameters fixed. Seven factors including the hole depth, burden, spacing, stemming, maximum charge per delay, charge and distance, were taken into account as the input parameters of the ANFIS model, whereas the peak particle velocity is the output parameter. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the ground vibration due to blasting.

关键词： Adaptive neuro-fuzzy inference system Ground vibration Blasting hybrid learning algorithm

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Real-time probabilistic sediment concentration forecasting using integrated dynamic network and error distribution heterogeneity

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International Journal of Sediment Research 2022年第6期37卷 766-779页

作者： Fangzheng Zhao Xinyu Wan Xiaolin Wang Qingyang Wu Yan Wu College of Hydrology and Water Resources Hohai University

Sediment forecasting at a dam site is important for the operation and management of water and sediment in a reservoir. However, the forecast results generally have some uncertainties, which may hinder the operation of the dam. In this study, a real-time sediment concentration probabilistic forecasting model is proposed based on a dynamic network model. Under this framework, the Elman neural network(ENN) and nonlinear auto-regressive with exogenous inputs(NARX) neural network models were established for sediment concentration forecasting with different lead times. A hybrid algorithm, which combined the Levenberg-Marquardt algorithm and real-time recurrent learning, was used to train the model. Using the aforementioned method, the sediment concentration was forecast for at the Sanmenxia Dam, China, and, subsequently, the forecast results were evaluated. Among the selected lead time, the results at 5 h exhibited the highest accuracy and practical significance. Compared with the ENN model,the sediment concentration peak error using the NARX neural network was reduced by 4.5%, and the sediment yield error was reduced by 0.043%. Therefore, the NARX neural network was selected as the deterministic sediment forecasting model. Additionally, the probability density function of the sediment concentration was derived based on the heterogeneity of the error distribution, and the sediment concentration interval, with different confidence levels, expected values, and median values, was forecast. The Nash-Sutcliffe coefficient of efficiency for the sediment concentration, as forecasted based on the median value, was the highest(0.04 higher than that using a deterministic model), whereas the error of the sediment concentration peak and sediment yield remained unaltered. These results indicated the accuracy and superiority of the proposed real-time sediment probabilistic forecasting hybrid model.

关键词： Dynamic network hybrid learning algorithm Error distribution heterogeneity Probability density function Sediment concentration forecasting NARX neural network

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