Abstract: In this article the use of discrete simulation in Assembly Line Balancing (ALB) will be... more Abstract: In this article the use of discrete simulation in Assembly Line Balancing (ALB) will be considered, thus by avoiding from complicated algorithms in line balancing field it has been tried to present a simulation model with Visual Slam which has an ability to consider different layouts and alternatives of allocation of resources (operators, apparatus, instrument,…).
Abstract—Researchers have been continuously trying to improve human performance with respect to H... more Abstract—Researchers have been continuously trying to improve human performance with respect to Health, Safety, Environment, Ergonomics (HSEE) and International Organization for Standardization (ISO). This study proposes a flexible intelligent algorithm for assessment and optimization of demographic features on integrated HSEE-ISO systems among operators of a gas transmission refinery.
Abstract-Generally, truncations of α has been used to study the fuzzy regression model. In this p... more Abstract-Generally, truncations of α has been used to study the fuzzy regression model. In this paper, fuzzy regression is accomplished by the fuzzy neural networks and the necessary neural nets training is proposed by the fuzzy numbers which is based on genetic algorithm. The proposed neural net learning method based on GA is claimed to be a better substitute because of its higher efficiency.
Abstract—Educational information system has a high effect on service quality of official departme... more Abstract—Educational information system has a high effect on service quality of official departments in universities and student satisfaction. This study proposes an intelligent algorithm for measuring and enhancing student satisfaction from educational information system. To achieve the objectives of this study, a standard questionnaire is completed by students.
Genetic Algorithm (GA) for efficiency assessment and optimization of electricity transmission uni... more Genetic Algorithm (GA) for efficiency assessment and optimization of electricity transmission units. Performance of 16 regional electricity companies (RECs) is evaluated by GA and the non-parametric technique of DEA. The results indicate that the performance of several companies is sub-optimal, suggesting the potential for significant cost reduction and reduction in employee number. The optimization procedure in this paper is followed from two different viewpoints ie input efficiency and input cost.
Abstract G. Lakoff in his paper [1] discussed in depth the concept of natural logic whose goals a... more Abstract G. Lakoff in his paper [1] discussed in depth the concept of natural logic whose goals are the following: to express all concepts capable of being expressed in natural language, to characterize all the valid inferences that can be made in natural language, and to mesh with adequate linguistic descriptions of all natural languages.
Abstract-This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-te... more Abstract-This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electricity consumption prediction. Six models are proposed to forecast annual Electricity demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electricity consumption. Two parameters have been considered in constructing and examination of plausible ANFIS models. Type of membership function and number of linguistic variables are two mentioned parameters.
Abstract-Fuzzy optimization deals with finding the values of input parameters of a complex simula... more Abstract-Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which result in desired output. Traditional techniques may require an enormous amount of simulation runs to evaluate the system. To alleviate this problem, the proposed work provides the means of incorporating knowledge, expressed in natural language, which is often available among analysts and decision makers.
Abstract: This study proposes a method, using adaptive neural network (ANN), to predict, estimate... more Abstract: This study proposes a method, using adaptive neural network (ANN), to predict, estimate and evaluate performancevariables without requiring any restrictive assumptions, taking case of a railway system. Also, by means of this method, it wouldbe possible to compare actual performance data with estimated values and route their assignable causes in future periods. Energyconsumption norm of vehicles in case of energy railway and real data of energy consumption in Iranian railway is considered.
Abstract: Fault detection and diagnosis has an effective role for the safe operation and long lif... more Abstract: Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration.
Abstract Researchers have been continuously trying to improve human performance with respect to H... more Abstract Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). This study proposes an adaptive neural network (ANN) algorithm for measuring and improving job satisfaction among operators with respect to HSEE in a gas refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators.
ABSTRACT The objective of this study is to examine the impacts of Emotional Learning based Fuzzy ... more ABSTRACT The objective of this study is to examine the impacts of Emotional Learning based Fuzzy Inference System (ELFIS) on gas consumption estimation. In this survey we estimate long-term natural gas (NG) demand of Iran. To meet this purpose we apply intelligent computer based approach. This study implies four soft computing methods which are Artificial Neural Network (ANI\ D, Adaptive Neuro-Fuzzy Inference System (ANF IS), ELFIS and Conventional Regression to forecast the yearly NG demand of Iran.
Abstract: In this article the use of discrete simulation in Assembly Line Balancing (ALB) will be... more Abstract: In this article the use of discrete simulation in Assembly Line Balancing (ALB) will be considered, thus by avoiding from complicated algorithms in line balancing field it has been tried to present a simulation model with Visual Slam which has an ability to consider different layouts and alternatives of allocation of resources (operators, apparatus, instrument,…).
Abstract—Researchers have been continuously trying to improve human performance with respect to H... more Abstract—Researchers have been continuously trying to improve human performance with respect to Health, Safety, Environment, Ergonomics (HSEE) and International Organization for Standardization (ISO). This study proposes a flexible intelligent algorithm for assessment and optimization of demographic features on integrated HSEE-ISO systems among operators of a gas transmission refinery.
Abstract-Generally, truncations of α has been used to study the fuzzy regression model. In this p... more Abstract-Generally, truncations of α has been used to study the fuzzy regression model. In this paper, fuzzy regression is accomplished by the fuzzy neural networks and the necessary neural nets training is proposed by the fuzzy numbers which is based on genetic algorithm. The proposed neural net learning method based on GA is claimed to be a better substitute because of its higher efficiency.
Abstract—Educational information system has a high effect on service quality of official departme... more Abstract—Educational information system has a high effect on service quality of official departments in universities and student satisfaction. This study proposes an intelligent algorithm for measuring and enhancing student satisfaction from educational information system. To achieve the objectives of this study, a standard questionnaire is completed by students.
Genetic Algorithm (GA) for efficiency assessment and optimization of electricity transmission uni... more Genetic Algorithm (GA) for efficiency assessment and optimization of electricity transmission units. Performance of 16 regional electricity companies (RECs) is evaluated by GA and the non-parametric technique of DEA. The results indicate that the performance of several companies is sub-optimal, suggesting the potential for significant cost reduction and reduction in employee number. The optimization procedure in this paper is followed from two different viewpoints ie input efficiency and input cost.
Abstract G. Lakoff in his paper [1] discussed in depth the concept of natural logic whose goals a... more Abstract G. Lakoff in his paper [1] discussed in depth the concept of natural logic whose goals are the following: to express all concepts capable of being expressed in natural language, to characterize all the valid inferences that can be made in natural language, and to mesh with adequate linguistic descriptions of all natural languages.
Abstract-This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-te... more Abstract-This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electricity consumption prediction. Six models are proposed to forecast annual Electricity demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electricity consumption. Two parameters have been considered in constructing and examination of plausible ANFIS models. Type of membership function and number of linguistic variables are two mentioned parameters.
Abstract-Fuzzy optimization deals with finding the values of input parameters of a complex simula... more Abstract-Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which result in desired output. Traditional techniques may require an enormous amount of simulation runs to evaluate the system. To alleviate this problem, the proposed work provides the means of incorporating knowledge, expressed in natural language, which is often available among analysts and decision makers.
Abstract: This study proposes a method, using adaptive neural network (ANN), to predict, estimate... more Abstract: This study proposes a method, using adaptive neural network (ANN), to predict, estimate and evaluate performancevariables without requiring any restrictive assumptions, taking case of a railway system. Also, by means of this method, it wouldbe possible to compare actual performance data with estimated values and route their assignable causes in future periods. Energyconsumption norm of vehicles in case of energy railway and real data of energy consumption in Iranian railway is considered.
Abstract: Fault detection and diagnosis has an effective role for the safe operation and long lif... more Abstract: Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration.
Abstract Researchers have been continuously trying to improve human performance with respect to H... more Abstract Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). This study proposes an adaptive neural network (ANN) algorithm for measuring and improving job satisfaction among operators with respect to HSEE in a gas refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators.
ABSTRACT The objective of this study is to examine the impacts of Emotional Learning based Fuzzy ... more ABSTRACT The objective of this study is to examine the impacts of Emotional Learning based Fuzzy Inference System (ELFIS) on gas consumption estimation. In this survey we estimate long-term natural gas (NG) demand of Iran. To meet this purpose we apply intelligent computer based approach. This study implies four soft computing methods which are Artificial Neural Network (ANI\ D, Adaptive Neuro-Fuzzy Inference System (ANF IS), ELFIS and Conventional Regression to forecast the yearly NG demand of Iran.
This paper presents a genetic algorithm (GA)–principal component analysis (PCA) for long–term nat... more This paper presents a genetic algorithm (GA)–principal component analysis (PCA) for long–term natural gas (NG) consumption prediction and improvement. Six models are proposed to forecast the annual gas demand. Around 27 GAs have been constructed and tested in order to find the best GA for gas consumption. The proposed models consist of input variables such as gross domestic product (GDP) and population (POP). All of trained GAs are then compared with each other respect to the mean absolute percentage error (MAPE). The GA ...
JA Konrath's post is responding to Writers House president Simon Lipskar's “all books a... more JA Konrath's post is responding to Writers House president Simon Lipskar's “all books are fungible” theory (written in support of big publishers and Apple over the DoJ suite), which claimed that “consumers can't be harmed by some books being priced higher because books are essentially fungible.” ... Konrath says, “on a personal note… Lipskar's argument makes me sad. Not just because, in suggesting that books are fungible, Lipskar implicitly devalues them.” ... Of course, people are pissed at the idea of books being interchangeable, but is that the true ...
This study presents an integrated fuzzy regression, computer simulation, and time series algorith... more This study presents an integrated fuzzy regression, computer simulation, and time series algorithm to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Since, it is difficult to model the uncertain behavior of energy consumption with only conventional fuzzy regression or time series, the integrated algorithm could be an ideal method for such cases. Computer simulation is developed to generate random ...
In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), an... more In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.
Utilization of small data sets for energy consumption forecasting is a major problem because it c... more Utilization of small data sets for energy consumption forecasting is a major problem because it could create large noise. This study presents a hybrid framework for improvement of energy consumption estimation with small data sets. The framework is based on fuzzy regression, conventional regression and design of experiment (DOE). The hybrid framework uses analysis of variance (ANOVA) and minimum absolute percentage error (MAPE) to select between fuzzy and conventional regressions. The significance of the proposed framework is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE. Second, the framework may identify conventional regression as the best model for future energy consumption forecasting because of its dynamic structure, whereas in the case of uncertainty and ambiguity, previous studies assume that fuzzy regression provides better solutions and estimation. Third, it is ideal candidate for short data sets. To show the applicability of the hybrid framework, the data for energy consumption in Canada, United States, Singapore, Pakistan and Iran from 1995 to 2005 are considered and tested. This is the first study which introduces a hybrid fuzzy regression-design of experiment for improvement of energy consumption estimation and forecasting with relatively small data sets.► The proposed framework is flexible and identifies the best model based on the results of ANOVA and MAPE. ► The proposed model may identify classical regression as the best model for future energy consumption forecasting because of its dynamic structure, whereas in the case of uncertainty and ambiguity, previous studies assume that fuzzy regression provides better solutions and estimation. ► It is ideal for relatively small data sets.
Efficiency frontier analysis has been an important approach of evaluating firms’ performance in p... more Efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making units (DMUs) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). An example using real data is presented for illustrative purposes. In the application to the power generation sector of Iran, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. Moreover, principle component analysis (PCA) is used to verify the findings of the proposed algorithm.
Uploads
Books by Morteza Saberi
Papers by Morteza Saberi