The Experts below are selected from a list of 2445 Experts worldwide ranked by ideXlab platform
Dariush Mowla - One of the best experts on this subject based on the ideXlab platform.
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experimental and theoretical investigation of shelled corn drying in a microwave assisted fluidized bed dryer using artificial neural network
Food and Bioproducts Processing, 2011Co-Authors: Leila Momenzadeh, Ali Zomorodian, Dariush MowlaAbstract:Abstract Drying characteristics of shelled corn (Zea mays L) with an initial moisture content of 26% dry basis (db) was studied in a fluidized bed dryer assisted by microwave heating. Four air temperatures (30, 40, 50 and 60 °C) and five microwave powers (180, 360, 540, 720 and 900 W) were studied. Several experiments were conducted to obtain data for sample moisture content versus drying time. The results showed that increasing the drying air temperature resulted in up to 5% decrease in drying time while in the microwave-assisted fluidized bed system, the drying time decreased dramatically up to 50% at a given and corresponding drying air temperature at each microwave energy level. As a result, addition of microwave energy to the fluidized bed drying is recommended to enhance the drying rate of shelled corn. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the drying time (output parameter for ANN modeling) was investigated. Microwave power, drying air temperature and grain moisture content were considered as input parameters for the model. An ANN model with 170 neurons was selected for studying the influence of Transfer Functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent Sigmoid) Transfer Function and trainrp (Resilient back propagation) back propagation algorithm made the most accurate predictions for the shelled corn drying system. The effects of uncertainties in output experimental data and ANN prediction values on root mean square error (RMSE) were studied by introducing small random errors within a range of ±5%.
Youngah Park - One of the best experts on this subject based on the ideXlab platform.
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generalization in a perceptron with a Sigmoid Transfer Function
International Joint Conference on Neural Network, 1993Co-Authors: Sanghun Ha, Kukjin Kang, Jonghoon Oh, Chulan Kwon, Youngah ParkAbstract:Learning of layered neural networks is studied using the methods of statistical mechanics. Networks are trained from examples using the Gibbs algorithm. We focus on the generalization curve, i.e. the average generalization error as a Function of the number of the examples. We consider perceptron learning with a Sigmoid Transfer Function. Ising perceptrons, with weights constrained to be discrete, exhibit sudden learning at low temperatures within the annealed approximation. There is a first order transition from a state of poor generalization to a state of perfect generalization. When the Transfer Function is smooth, the first order transition occurs only at low temperatures. The transition becomes continuous at high temperatures. When the Transfer Function is steep, the first order transition line is extended to the higher temperature. The analytic results show a good agreement with the computer simulations.
Leila Momenzadeh - One of the best experts on this subject based on the ideXlab platform.
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experimental and theoretical investigation of shelled corn drying in a microwave assisted fluidized bed dryer using artificial neural network
Food and Bioproducts Processing, 2011Co-Authors: Leila Momenzadeh, Ali Zomorodian, Dariush MowlaAbstract:Abstract Drying characteristics of shelled corn (Zea mays L) with an initial moisture content of 26% dry basis (db) was studied in a fluidized bed dryer assisted by microwave heating. Four air temperatures (30, 40, 50 and 60 °C) and five microwave powers (180, 360, 540, 720 and 900 W) were studied. Several experiments were conducted to obtain data for sample moisture content versus drying time. The results showed that increasing the drying air temperature resulted in up to 5% decrease in drying time while in the microwave-assisted fluidized bed system, the drying time decreased dramatically up to 50% at a given and corresponding drying air temperature at each microwave energy level. As a result, addition of microwave energy to the fluidized bed drying is recommended to enhance the drying rate of shelled corn. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the drying time (output parameter for ANN modeling) was investigated. Microwave power, drying air temperature and grain moisture content were considered as input parameters for the model. An ANN model with 170 neurons was selected for studying the influence of Transfer Functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent Sigmoid) Transfer Function and trainrp (Resilient back propagation) back propagation algorithm made the most accurate predictions for the shelled corn drying system. The effects of uncertainties in output experimental data and ANN prediction values on root mean square error (RMSE) were studied by introducing small random errors within a range of ±5%.
Sanghun Ha - One of the best experts on this subject based on the ideXlab platform.
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generalization in a perceptron with a Sigmoid Transfer Function
International Joint Conference on Neural Network, 1993Co-Authors: Sanghun Ha, Kukjin Kang, Jonghoon Oh, Chulan Kwon, Youngah ParkAbstract:Learning of layered neural networks is studied using the methods of statistical mechanics. Networks are trained from examples using the Gibbs algorithm. We focus on the generalization curve, i.e. the average generalization error as a Function of the number of the examples. We consider perceptron learning with a Sigmoid Transfer Function. Ising perceptrons, with weights constrained to be discrete, exhibit sudden learning at low temperatures within the annealed approximation. There is a first order transition from a state of poor generalization to a state of perfect generalization. When the Transfer Function is smooth, the first order transition occurs only at low temperatures. The transition becomes continuous at high temperatures. When the Transfer Function is steep, the first order transition line is extended to the higher temperature. The analytic results show a good agreement with the computer simulations.
Mehmet Özalp - One of the best experts on this subject based on the ideXlab platform.
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Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks
Energy Conversion and Management, 2004Co-Authors: Adnan Sözen, Erol Arcaklioglu, Mehmet ÖzalpAbstract:Thermodynamic analysis of absorption thermal systems is too complex because the analytic Functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (i) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption heat pump (EAHP). The ejector has two Functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic Functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three different variants and logistic Sigmoid Transfer Function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COPr), exergetic coefficient of performance of the system for cooling (ECOPr) and circulation ratio (F) for all working temperatures were found to be less than 1.7%, 5.1%, and 1.9%, respectively. Deviations for COPr, ECOPr and F at a generator temperature of ∼90 °C (cut off temperature) at which the coefficient of performance of the system is maximum are 0.9%, 1.8%, and 0.1%, respectively, for other working temperatures. When this system was used for heating, similar deviations were obtained. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties. The results showed that the use of ANNs for determination of thermodynamic properties is acceptable in design of the EAHP.