Search for: backpropagation-algorithms
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    Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect

    , Article Journal of Materials Processing Technology ; Volume 196, Issue 1-3 , 2008 , Pages 115-119 ; 09240136 (ISSN) Sheikh, H ; Serajzadeh, S ; Sharif University of Technology
    In this work, neural networks are used for estimation of flow stress of AA5083 with regard to dynamic strain ageing that occurs in certain deformation conditions and varies flow stress behavior of the metal being deformed. The input variables are selected to be strain rate, temperature and strain and the output value is the flow stress. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow and the smooth yielding domains different neural networks are constructed based on the achieved results. While a feed-forward backpropagation algorithm is... 

    Modeling the correlation between heat treatment, chemical composition and bainite fraction of pipeline steels by means of artificial neural networks

    , Article Neural Network World ; Volume 23, Issue 4 , 2013 , Pages 351-367 ; 12100552 (ISSN) Khalaj, G ; Pouraliakbar, H ; Mamaghani, K. R ; Khalaj, M. J ; Sharif University of Technology
    In the present study, bainite fraction results of continuous cooling of high strength low alloy steels have been modeled by artificial neural networks. The artificial neural network models were constructed by 16 input parameters including chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), Nb in solution, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. The value for the output layer was the bainite fraction. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. To make a decision on the completion... 

    Voice conversion using nonlinear principal component analysis

    , Article 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, Honolulu, HI, 1 April 2007 through 5 April 2007 ; 2007 , Pages 336-339 ; 1424407079 (ISBN); 9781424407071 (ISBN) Makki, B ; Seyed salehi, S. A ; Sadati, N ; Noori Hosseini, M ; Sharif University of Technology
    In the last decades, much attention has been paid to the design of multi-speaker voice conversion. In this work, a new method for voice conversion (VC) using nonlinear principal component analysis (NLPCA) is presented. The principal components are extracted and transformed by a feed-forward neural network which is trained by combination of Genetic Algorithm (GA) and Back-Propagation (BP). Common pre- and post-processing approaches are applied to increase the quality of the synthesized speech. The results indicate that the proposed method can be considered as a step towards multi-speaker Voice conversion. © 2007 IEEE  

    Neural network and neuro-fuzzy assessments for scour depth around bridge piers

    , Article Engineering Applications of Artificial Intelligence ; Volume 20, Issue 3 , 2007 , Pages 401-414 ; 09521976 (ISSN) Bateni, S. M ; Borghei, S. M ; Jeng, D. S ; Sharif University of Technology
    The mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. Hence, in this study, alternative approaches, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), are proposed to estimate the equilibrium and time-dependent scour depth with numerous reliable data base. Two ANN models, multi-layer perception using back-propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), were used. The equilibrium scour depth was... 

    Adaptive nonlinear observer design using feedforward neural networks

    , Article Scientia Iranica ; Volume 12, Issue 2 , 2005 , Pages 141-150 ; 10263098 (ISSN) Dehghan Nayeri, M. R ; Alasty, A ; Sharif University of Technology
    Sharif University of Technology  2005
    This paper concerns the design of a neural state observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters... 

    Modelling of conventional and severe shot peening influence on properties of high carbon steel via artificial neural network

    , Article International Journal of Engineering, Transactions B: Applications ; Volume 31, Issue 2 , 2018 , Pages 382-393 ; 1728144X (ISSN) Maleki, E ; Farrahi, G. H ; Sharif University of Technology
    Materials and Energy Research Center  2018
    Shot peening (SP), as one of the severe plastic deformation (SPD) methods is employed for surface modification of the engineering components by improving the metallurgical and mechanical properties. Furthermore, artificial neural network (ANN) has been widely used in different science and engineering problems for predicting and optimizing in the last decade. In the present study, effects of conventional shot peening (CSP) and severe shot peening (SSP) on properties of AISI 1060 high carbon steel were modelled and compared via ANN. In order to networks training, the back propagation (BP) error algorithm is developed and data of experimental tests results are employed. Experimental data... 

    Prediction of BLEVE mechanical energy by implementation of artificial neural network

    , Article Journal of Loss Prevention in the Process Industries ; Volume 63 , January , 2020 Hemmatian, B ; Casal, J ; Planas, E ; Hemmatian, B ; Rashtchian, D ; Sharif University of Technology
    Elsevier Ltd  2020
    In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input... 

    Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders

    , Article Materials Science and Engineering A ; Volume 466, Issue 1-2 , 2007 , Pages 274-283 ; 09215093 (ISSN) Dashtbayazi, M. R ; Shokuhfar, A ; Simchi, A ; Sharif University of Technology
    An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer... 

    B-Jump: Roller length, sequent depth, and relative energy loss using artificial neural networks

    , Article Journal of Hydraulic Research ; Volume 45, Issue 4 , 2007 , Pages 529-537 ; 00221686 (ISSN) Yazdandoost, F. Y ; Bateni, S. M ; Fazeli, M ; Sharif University of Technology
    International Association of Hydraulic Engineering Research  2007
    The phenomenon of the hydraulic jump is so complex that despite considerable laboratory and prototype studies, estimation of its main characteristics in a generalized and accurate form is still difficult. The Artificial Neural Network (ANN) approach aims at limiting the needs for costly and time-consuming experiments. In this study, two ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis function using orthogonal least-squares algorithm (RBF/OLS), were used to predict the roller length, sequent depth, and the relative energy loss of the B-jump. Based on a pre-specified range of jump parameters, the input vectors include: upstream bed slope (tan θ),... 

    Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions

    , Article Journal of Natural Gas Science and Engineering ; Volume 21 , November , 2014 , Pages 19-25 ; ISSN: 18755100 Hamzehie, M. E ; Mazinani, S ; Davardoost, F ; Mokhtare, A ; Najibi, H ; Van der Bruggen, B ; Darvishmanesh, S ; Sharif University of Technology
    Absorption of carbon dioxide (CO2) in aqueous solutions can be improved by the addition of other compounds. However, this requires a large amount of equilibrium data for solubility estimation in a wide ranges of temperature, pressure and concentration. In this paper, a model based on an artificial neural network (ANN) was proposed and developed with mixtures containing monoethanolamine (MEA), diethanolamine (DEA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP), methanol, triethanolamine (TEA), piperazine (PZ), diisopropanolamine (DIPA) and tetramethylensulfone (TMS) to predict solubility of CO2 in mixed aqueous solution (especially in binary and ternary mixtures) over wide... 

    Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    , Article 4th Global Conference on Materials Science and Engineering, CMSE 2015, 3 August 2015 through 6 August 2015 ; Volume 103, Issue 1 , December , 2015 ; 17578981 (ISSN) Maleki, E ; Ashton A ; Ruda H. E ; Khotsianovsky A ; Sharif University of Technology
    Institute of Physics Publishing  2015
    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI)... 

    Application of artificial neural network to estimate the fatigue life of shot peened Ti-6Al-4V ELI alloy

    , Article Fatigue of Materials: Advances and Emergences in Understanding, Held During Materials Science and Technology 2010, MS and T'10, 17 October 2010 through 21 October 2010 ; 2010 , Pages 411-417 ; 9780470943182 (ISBN) Yavari, S. A ; Saeidi, N ; Maddah Hosseini, S. H ; Sharif University of Technology
    An artificial neural network to predict the fatigue life, residual stress and Almen intensity of shot peened alloy Ti6Al4V ELI was developed. To minimize the prediction error, a feed forward model was used and the neural network was trained with back-propagation learning Algorithm. The results of this investigation show that a neural network with one hidden layer and five neurons in this layer will give the best performance. With this structure the network approaches to the desired error in the least time. Furthermore, it was concluded that there is a good agreement between the experimental data, the predicted values and the well-trained neural network. Therefore, the neural network has a... 

    Neural networks control of autonomous underwater vehicle

    , Article ICMEE 2010 - 2010 2nd International Conference on Mechanical and Electronics Engineering, Proceedings, 1 August 2010 through 3 August 2010 ; Volume 2 , August , 2010 , Pages V2117-V2121 ; 9781424474806 (ISBN) Amin, R ; Khayyat, A. A ; Ghaemi Osgouie, K ; Sharif University of Technology
    This paper describes a neural network controller for autonomous underwater vehicles (AUVs). The designed online multilayer perceptron neural network (OMLPNN) calculates forces and moments in earth fixed frame to eliminate the tracking errors of AUVs whose dynamics are highly nonlinear and time varying. Another OMLPNN has been designed to generate an inverse model of AUV, which determine the appropriate propeller's speed and control surfaces' angles receiving the forces and moments in the body fixed frame. The designed approximation based neural network controller with the use of the backpropagation learning algorithm has advantages and robustness to control the highly nonlinear dynamics of... 

    A hierarchical artificial neural network for transport energy demand forecast: Iran case study

    , Article Neural Network World ; Volume 20, Issue 6 , 2010 , Pages 761-772 ; 12100552 (ISSN) Kazemi, A ; Shakouri, H .G ; Menhaj, M. B ; Mehregan, M. R ; Neshat, N ; Asgharizadeh, E ; Taghizadeh, M. R ; Sharif University of Technology
    This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy... 

    Artificial neural network modeling of Pt/C cathode degradation in pem fuel cells

    , Article Journal of Electronic Materials ; Volume 45, Issue 8 , 2016 , Pages 3822-3834 ; 03615235 (ISSN) Maleki, E ; Maleki, N ; Sharif University of Technology
    Springer New York LLC  2016
    Use of computational modeling with a few experiments is considered useful to obtain the best possible result for a final product, without performing expensive and time-consuming experiments. Proton exchange membrane fuel cells (PEMFCs) can produce clean electricity, but still require further study. An oxygen reduction reaction (ORR) takes place at the cathode, and carbon-supported platinum (Pt/C) is commonly used as an electrocatalyst. The harsh conditions during PEMFC operation result in Pt/C degradation. Observation of changes in the Pt/C layer under operating conditions provides a tool to study the lifetime of PEMFCs and overcome durability issues. Recently, artificial neural networks... 

    A Study on Flow Behavior of AA5086 Over a Wide Range of Temperatures

    , Article Journal of Materials Engineering and Performance ; Volume 25, Issue 3 , 2016 , Pages 1076-1084 ; 10599495 (ISSN) Asgharzadeh, A ; Jamshidi Aval, H ; Serajzadeh, S ; Sharif University of Technology
    Springer New York LLC  2016
    Flow stress behavior of AA5086 was determined using tensile testing at different temperatures from room temperature to 500 °C and strain rates varying between 0.002 and 1 s−1. The strain rate sensitivity parameter and occurrence of dynamic strain aging were then investigated in which an Arrhenius-type model was employed to study the serrated flow. Additionally, hot deformation behavior at temperatures higher than 320 °C was evaluated utilizing hyperbolic-sine constitutive equation. Finally, a feed forward artificial neural network model with back propagation learning algorithm was proposed to predict flow stress for all deformation conditions. The results demonstrated that the strain rate... 

    Experimental investigation and artificial neural network modeling of warm galvanization and hardened chromium coatings thickness effects on fatigue life of AISI 1045 carbon steel

    , Article Journal of Failure Analysis and Prevention ; Volume 17, Issue 6 , 2017 , Pages 1276-1287 ; 15477029 (ISSN) Kashyzadeh, K. R ; Maleki, E ; Sharif University of Technology
    In the present study, the main purpose is investigation of the coatings thickness effect on the fatigue life of AISI 1045 steel. Herein, two different coatings of warm galvanization and hardened chromium have been used on the specimens. Fatigue tests were performed on specimens with different coating thicknesses of 13 and 19 µm. In the high-cycle level, S–N curves are extracted with 13 points for each sample. The results show that the galvanized coating is the most appropriate coating with low thickness, but with significant increasing of coating thickness, the best choice is hardened chromium coating. However, artificial neural network (ANN) has been used as an efficient approach instead of... 

    Analysis of the effect of reinforcement particles on the compressibility of Al-SiC composite powders using a neural network model

    , Article Materials and Design ; Volume 30, Issue 5 , 2009 , Pages 1518-1523 ; 02641275 (ISSN) Hafizpour, H. R ; Sanjari, M ; Simchi, A ; Sharif University of Technology
    A neural network (ANN) model was developed to predict the densification of composite powders in a rigid die under uniaxial compaction. Al-SiC powder mixtures with various reinforcement volume fractions (0-30%) and particle sizes (50 nm to 40 μm) were prepared and their compressibility was studied in a wide range of compaction pressure up to 400 MPa. The experimental results were used to train a back propagation (BP) learning algorithm with two hidden layers. A sigmoid transfer function was developed and found to be suitable for analyzing the compressibility of composite powders with the least error. The trained model was used to study the effect of reinforcement particle size and volume... 

    A novel enzyme based biosensor for catechol detection in water samples using artificial neural network

    , Article Biochemical Engineering Journal ; Volume 128 , 2017 , Pages 1-11 ; 1369703X (ISSN) Maleki, N ; Kashanian, S ; Maleki, E ; Nazari, M ; Sharif University of Technology
    Elsevier B.V  2017
    Biosensors could be used as digital devices to measure the sample infield. Consequently, computational programming along with experimental achievements are required. In this study, a novel biosensor/artificial neural network (ANN) integrated system was developed. Poly (3,4-ethylenedioxy-thiophene)(PEDOT), graphene oxide nano-sheets (GONs) and laccase (Lac) were used to construct a biosensor. The simple and one-pot process was accomplished by electropolymerizing 3,4-ethylenedioxy-thiophene (EDOT) along with GONs and Lac as dopants on glassy carbon electrode. Scanning electron microscopy (SEM) and electrochemical characterization were conducted to confirm successful enzyme entrapment. The... 

    Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods

    , Article Colloids and Surfaces A: Physicochemical and Engineering Aspects ; Volume 541 , 2018 , Pages 154-164 ; 09277757 (ISSN) Ahmadi, M. H ; Alhuyi Nazari, M ; Ghasempour, R ; Madah, H ; Shafii, M. B ; Ahmadi, M. A ; Sharif University of Technology
    Elsevier B.V  2018
    Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The...