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A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

APPLIED soft computing 2016年 38卷 477-486页

作者： Han, Hong-Gui Li, Ying Guo, Ya-Nan Qiao, Jun-Fei Beijing Univ Technol Coll Elect & Control Engn Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values. (C) 2015 Elsevier B.V. All rights reserved.

关键词： soft computing method Recurrent self organizing neural network Sludge volume index Prediction Sensitivity analysis

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Evaluation of Topographical Irregularities Effect on Site Seismic Response Using soft computing method

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2022年第5期40卷 2737-2750页

作者： Karkhaneh, Sama Jahromi, Saeed Ghaffarpour Tarighat, Amir Shahid Rajaee Teacher Training Univ Dept Civil Engn Tehran Iran

Investigation of earthquake effect is one of the important stages in structural design and geotechnical engineering. In this study, we used the numerical method and soft computing method to predict the seismic response of topographical irregularities of the surface ground. Geometrical and geotechnical properties were used as the model inputs and the prediction of site seismic response amplification was regarded as its output. The findings indicated the precise performance of the seismic response prediction system as the fuzzy system accurately and appropriately covers all database information and can be applied in a wide and consistent range of inputs. The results showed good accordance with real observations and can be used for seismic designs.

关键词： Topography effects Site seismic response Finite element method soft computing method Fuzzy inference system

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Prediction of swelling pressures of expansive soils using soft computing methods

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NEURAL computing & APPLICATIONS 2014年第2期24卷 473-485页

作者： Ikizler, S. Banu Vekli, Mustafa Dogan, Emrah Aytekin, Mustafa Kocabas, Fikret Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey Bozok Univ Dept Civil Engn Yozgat Turkey Sakarya Univ Dept Civil Engn Fac Engn TR-54187 Sakarya Turkey Univ Bahrain Dept Civil Engn & Architecture Isa Town Bahrain Bartin Univ Dept Civil Engn Bartin Turkey

Lateral and vertical swelling pressures associated with expansive soils cause damages on structures. These pressures must be predicted before the structures are constructed in order to prevent the damages. The magnitude of the stresses can decrease rapidly when volume changes are partly allowed. Therefore, a material, which has a high compressibility, must be placed between expansive soils and the structures in both horizontal and vertical directions in order to decrease transmitted swelling pressure on structures. There are numerous techniques recommended for estimating the swelling pressures. However, these techniques are very complex and time-consuming. In this study, a new estimation model to predict the pressures is developed using experimental data. The data were collected in the laboratory using a newly developed device and experimental setup also. In the experimental setup, a rigid steel box was designed to measure transmitted swelling pressures in lateral and vertical directions. In the estimation model, approaches of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are employed. In the first stage of the study, the lateral and vertical swelling pressures were measured with different thicknesses of expanded polystyrene geofoam placed between one of the vertical walls of the steel box and the expansive soil in the laboratory. Then, ANN and ANFIS approaches were trained using these results of the tests measured in the laboratory as input for the prediction of transmitted lateral and vertical swelling pressures. Results obtained showed that ANN-based prediction and ANFIS approaches could satisfactorily be used to estimate the transmitted lateral and vertical swelling pressures of expansive soils.

关键词： Expanded polystyrene foam soft computing method Artificial neural network Adaptive neuro-fuzzy inference systems Expansive soil Swelling pressure

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Application of soft computing methods to Detect Fault in A.C Motor 5

Application of Soft Computing Methods to Detect Fault in A.C...

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5th IEEE International Conference on Advances in computing, Communication and Control (ICAC3)

作者： Puhan, Pratap Sekhar Behera, Sudersan Sreenidhi Inst Sci & Technol Elect & Elect Engn Dept Hyderabad 501301 Telengana India Sreenidhi Inst Sci & Technol Elect & Comp Engn Dept Hyderabad 501301 Telengana India

ISBN: (纸本)9781538638521

The paper investigates the effectiveness of soft computing methods to detect the fault in A.C. Motor. In this paper Feed Forward Back Propagation (FFBP) and Radial Basis Function (RBF) Neural Network controller is used as soft computing method and Three Phase Induction motor is used as AC Motor. Both the neural network controller is used here as classifier to classify the healthy and unhealthy condition of the motor. Discrimination of the inner race and outer race bearing fault of the proposed motor is carried out using Park's Transformation with motor steady current signals. Both controllers' gives good results for the above purpose but The RBF neural networks shows very encouraging results for multi-class classification problems. which proves the effectiveness of the controller for setting up a base in incipient fault detection of Induction Motor

关键词： soft computing method Neural Network Controller Park's Transform Induction Motor

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Fuzzy cognitive map based approach for determining the risk of ischemic stroke

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IET SYSTEMS BIOLOGY 2019年第6期13卷 297-304页

作者： Khodadadi, Mahsa Shayanfar, Heidarali Maghooli, Keivan Hooshang Mazinan, Amir Islamic Azad Univ South Tehran Branch Dept Control Engn Tehran Iran Iran Univ Sci & Technol Coll Elect Engn Ctr Excellence Power Automat & Operat Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Biomed Engn Tehran Iran

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 +/- 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

关键词： patient diagnosis fuzzy logic diseases medical computing cognition learning (artificial intelligence) fuzzy set theory Hebbian learning neural nets support vector machines ischemic stroke controllable factors uncontrollable factors blood pressure heart disease intelligent system immediate treatment soft computing method fuzzy cognitive mapping nonlinear Hebbian learning method fuzzy cognitive maps training risk rate

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Model tree approach for prediction of pile groups scour due to waves

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OCEAN ENGINEERING 2011年第13期38卷 1522-1527页

作者： Etemad-Shahidi, A. Ghaemi, N. Iran Univ Sci & Technol Sch Civil Engn Envirohydroinformat COE Tehran Iran

Scour around piles could endanger the stability of the structures placed on them. Hence, an accurate estimation of the scour depth around piles is very important in coastal and marine engineering. Due to the complex interaction between the wave, seabed and pile group;prediction of the scour depth is not an easy task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been used for the prediction of the scour depth. However, these methods do not give enough insight about the process and are not as easy to use as the empirical equations. In this study, new formulas are given that are easy to use, accurate and physically sound. Available empirical equations for estimating the pile group scour depth such as those of Sumer et al. (1992) and Bayram and Larson (2000), are less accurate compared to the given equations. These equations are as accurate as other soft computing methods such as ANN and SVM. Moreover, in this study, safety factors are given for different levels of acceptable risks, which can be so useful for engineers. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Scour depth Pile group soft computing method Model tree Probabilistic design

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Appraisal of Takagi-Sugeno-Kang type of adaptive neuro-fuzzy inference system for draft force prediction of chisel plow implement

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年第PartA期142卷 406-415页

作者： Shafaei, S. M. Loghavi, M. Kamgar, S. Shiraz Univ Dept Biosyst Engn Sch Agr Shiraz *** Iran

Required draft force of chisel plow implement during tillage operations was comprehensively apprised. Field experiments were carried out at three levels of plowing depth (PD) (10, 20 and 30 (cm)) and three levels of forward speed (FS) (2, 4 and 6 (km/h)) in a clay loam soil. An intelligent model based on soft computing technique, adaptive neuro-fuzzy inference system (ANFIS), was used to integrally predict draft force. The FS and PD were chosen as input variables and the draft force was considered as output parameter in the first order Takagi-Sugeno-Kang type of ANFIS model. A comparison was also performed between results of the best developed ANFIS model and those of the well-known mathematical model suggested by American Society of Agricultural and Biological Engineers (ASABE). To Select the best model with the highest predictive ability, some statistical performance criteria (SPG) (coefficient of determination (R-2), root mean square error (RMSE), mean relative deviation modulus (MRDM), mean of absolute values of prediction residual errors (MAVPRE) and prediction error mean (PEM)) were used. The results demonstrated that the best ANFIS model with acceptable SPC values of R-2 = 0.994, RMSE = 0.722 (kN), MRDM = 3.172%, MAVPRE = 0.561 (kN) and PEM = -0.071% was more accurate than the ASABE model. The ANFIS modeling results also showed that the simultaneous or individual increment of FS and PD resulted in nonlinear increment of draft force. Additionally, the interaction of FS and PD on draft force was congruent. Application of physical perception obtained from developed ANFIS model results led .to exposition of a new scientific window towards deep root understanding of draft force behavior. Thus, it is practically proposed to employ the ANFIS model for proper selection of tractor type for pulling chisel plow Implement in the most efficient manner.

关键词： soft computing method Forward speed Plowing depth Single effect Dual interaction effect

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Artificial Neural Network (ANN) Based Prediction of Ultimate Axial Load Capacity of Concrete-Filled Steel Tube Columns (CFSTCs)

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2022年第5期22卷 1341-1358页

作者： Avci-Karatas, Cigdem Yalova Univ Fac Engn Dept Transportat Engn TR-77200 Yalova Turkey

Concrete-filled steel tube columns (CFSTCs) are preferred due to enhanced ductility and energy absorption. The capability of an artificial neural network (ANN) based analytical model on estimating the ultimate load capacity of circular CFSTCs under axial loadings has been investigated in this study. To provide a better prediction in modeling, 150 comprehensive experimental data were obtained from circular CFSTC's geometrical and mechanical properties, such as height, diameter, thickness, the yield stress of steel, unconfined concrete strength, Young's modulus of steel and concrete, etc., were examined. The backpropagation-training practice available in ANN was used to update the weights of each layer based on the network output error. For feedforward-backpropagation, the Levenberg-Marquardt algorithm was employed. The effectiveness of the ANN model was developed using general-purpose software MATLAB (R) by training and predicting the ultimate load capacity of circular CFSTCs. Finally, about 75% of the data were used for ANN training, and the remaining 25% was used for testing the ANN model. The results show that the predicted values of ultimate load capacity using the ANN model agree well with that of the corresponding experimental observations, and the percentage difference is about +/- 10%. Additionally, a new engineering index, a20-index, was predicted to further verify the reliability of the model. The findings of this article are new and will significantly contribute to the existing technology of ANN-based modeling in composite construction.

关键词： Concrete-filled steel tube column Composite structures Ultimate axial load capacity Artificial neural network (ANN) Statistical modeling tool soft computing method

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Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

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APPLIED soft computing 2015年 36卷 375-382页

作者： Fathabadi, Hassan Kharazmi Univ Dept Engn Tehran Iran

In this study, two approaches are presented to detect short-circuit faults in power transmission lines. The two proposed methods are completely novel from both theoretical and technical aspects. The first approach is a soft computing method that uses discrete wavelet transform with Daubechies mother wavelets dbl, db2, db3, and db4. The second approach is a hardware based method that utilizes a novel proposed two-stage finite impulse response filter with a sampling frequency of 32 kHz, and a very short process time about three samples time. The two approaches are analyzed by presenting theoretical results. Simulated results obtained by simulating a three-phase 230 kV, 50 Hz power transmission line are given that validate the theoretical results, and explicitly verify that the filter based approach has an accuracy of 100% in presence of 10% disturbance while the accuracy of the wavelet transform based approach is maximally 97%, but it has less complication and implementation cost. Another comparative study between this work and other works shows that the two proposed methods have higher accuracy and very shorter process time compared to the other methods, especially in presence of 10% disturbance that actually occurs in power transmission lines. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Discrete wavelet transform (DWT) soft computing method Filter based technique Power transmission lines Short-circuit fault detection

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Simplified swarm optimisation for the solar cell models parameter estimation problem

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IET RENEWABLE POWER GENERATION 2017年第8期11卷 1166-1173页

作者： Yeh, Wei-Chang Lin, Peijie Huang, Chia-Ling Natl Tsing Hua Univ Dept Ind Engn & Engn Management POB 24-60 Hsinchu 300 Taiwan Fuzhou Univ Sch Phys & Informat Engn 2 Xue Yuan Rd Fuzhou Fujian 350108 Peoples R China Kainan Univ Dept Logist & Shipping Management 1 Kainan Rd Taoyuan 33857 Taiwan

Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal parameters of the solar cell models should be estimated exactly. In this study, improved simplified swarm optimisation (iSSO), a recently introduced soft computing method based on simplified swarm optimisation, is proposed to minimise the least square error between the extracted and the measured data for the solar cell models parameter estimation of the single- and double-diode model problems. Based on the new all-variable difference update mechanism and survival of the fittest policy, the proposed algorithm is able to find an improved approximation for estimating the parameters of single- and double-diode solar cell models. As evidence of the utility of the proposed iSSO, the authors present extensive computational results for two benchmark problems. The comparison of the computational results supports the proposed iSSO algorithm outperforms the previously developed algorithms for all of the experiments in the literature.

关键词： solar cells power system parameter estimation minimisation least squares approximations photovoltaic power systems power engineering computing diodes solar cell model parameter estimation problem solar energy application PV system performance optimisation photovoltaic system performance optimisation improved simplified swarm optimisation soft computing method least square error minimisation single-diode model problem double-diode model problem all-variable difference update mechanism iSSO algorithm

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