http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Jamal Shahrabi,Sara Mottaghi Khameneh 대한산업공학회 2016 Industrial Engineeering & Management Systems Vol.15 No.4
Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.
Shahrabi, Jamal,Khameneh, Sara Mottaghi Korean Institute of Industrial Engineers 2016 Industrial Engineeering & Management Systems Vol.15 No.4
Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.
Design Optimization of a Centrifugal Pump Using Particle Swarm Optimization Algorithm
Mohamad Bashiri,Shahram Derakhshan,Jamal Shahrabi 한국유체기계학회 2019 International journal of fluid machinery and syste Vol.12 No.4
In the many industries centrifugal pumps consume a lot of energy. So the efficiency optimization of these pumps is so important. In this study shape of impeller of centrifugal pump was optimized to increase efficiency and head. The evolutionary algorithm based on modified artificial neural network (ANN), particle swarm optimization (PSO) and validated CFD data were used to optimized shape of centrifugal impeller. The pump experimentally investigated in test rig and measured data were used to verify the numerical results. Finally, to verify optimization results the complete numerical characteristic curves of the initial pump were compared with the optimized pump. The numerical results of optimized pump depict that efficiency improved 3.2% and head increase 5.52(m).