The Experts below are selected from a list of 4872 Experts worldwide ranked by ideXlab platform
Jingwen Tian - One of the best experts on this subject based on the ideXlab platform.
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Building Logistics Cost Forecast Based on High Speed and Precise Genetic Algorithm Neural Network
2009 International Workshop on Intelligent Systems and Applications, 2009Co-Authors: Jingwen Tian, Jin XuAbstract:The building Logistics Cost forecasting was a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building Logistics Cost based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We constructed the network structure, and discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective.
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building Logistics Cost forecast based on high speed and precise genetic algorithm neural network
Information Security and Assurance, 2009Co-Authors: Jingwen Tian, Jin XuAbstract:The building Logistics Cost forecasting was a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building Logistics Cost based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We constructed the network structure, and discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective. There are many ways to forecast the Logistics, such as moving average method, exponential smoothing method, time series decomposition method, regression model method etc. These traditional methods are required to establish a clear functional relation with the forecasted goal, but the Logistics system is always an open complex systems. There are a number of factors including both qualitative factors and quantitative indicators impact of the changes in the demand. In addition, the development of the Logistics is always presented in a general non-linear and random state, and it is closely related to the level of economic development of the city, the government policies and other factors etc. These factors are not only difficult to describe quantitatively, but also difficult to establish the certain functional relation with the forecasted goal (2)-(3). So, it is not appropriate to use the aforementioned methods. The BP network based on gradient descend is a new
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Building Logistics Cost Forecast Based on Improved Simulated Annealing Neural Network
2009 Second International Conference on Intelligent Computation Technology and Automation, 2009Co-Authors: Jingwen TianAbstract:The building Logistics Cost forecasting is a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfractuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building Logistics Cost based on improved simulated annealing neural network (ISANN) is presented in this paper. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is organic combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm, instead of gradient falling algorithm of BP network to train network weight. It can get higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow, and discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and faster convergence of ISANN, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective.
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The Research of Building Logistics Cost Forecast Based on Regression Support Vector Machine
2009 International Conference on Computational Intelligence and Security, 2009Co-Authors: Jingwen Tian, Shiru ZhouAbstract:Building Logistics Cost forecasting is a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, so it was difficult to describe it by traditional methods. The support vector machine (SVM) has the ability of strong nonlinear function approach and strong generalization and it also has the feature of global optimization, in this paper, a modeling and forecasting method of building Logistics Cost based on SVM is presented. The SVM network structure for forecasting building Logistics Cost is established. Moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the convergence rate and the forecasting accuracy. We discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and well generalization of SVM, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective.
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The research of building Logistics Cost forecast based on radial basic probabilistic neural network
2009 IEEE International Conference on Automation and Logistics, 2009Co-Authors: Jingwen Tian, Shiru ZhouAbstract:The building Logistics Cost forecasting is a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, so it was difficult to describe it by traditional methods. Radial basic probabilistic neural network (RBPNN) is one of the neural networks used widely and it has the ability of strong function approach and fast convergence, in this paper, a modeling and forecasting method of building Logistics Cost based on RBPNN is presented. We construct the structure of radial basic probabilistic neural network that used for forecasting building Logistics Cost, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong function approach and fast convergence of radial basic probabilistic neural network, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective.
Jin Xu - One of the best experts on this subject based on the ideXlab platform.
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Building Logistics Cost Forecast Based on High Speed and Precise Genetic Algorithm Neural Network
2009 International Workshop on Intelligent Systems and Applications, 2009Co-Authors: Jingwen Tian, Jin XuAbstract:The building Logistics Cost forecasting was a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building Logistics Cost based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We constructed the network structure, and discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective.
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building Logistics Cost forecast based on high speed and precise genetic algorithm neural network
Information Security and Assurance, 2009Co-Authors: Jingwen Tian, Jin XuAbstract:The building Logistics Cost forecasting was a complicated nonlinear problem, due to the factors that influence building Logistics Cost are anfratuous, and it was difficult to describe it by traditional methods. So a modeling and forecasting method of building Logistics Cost based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We constructed the network structure, and discussed and analyzed the effect factor of building Logistics Cost. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the modeling and forecasting method can truly forecast the building Logistics Cost by learning the index information. The actual forecasting results show that this method is feasible and effective. There are many ways to forecast the Logistics, such as moving average method, exponential smoothing method, time series decomposition method, regression model method etc. These traditional methods are required to establish a clear functional relation with the forecasted goal, but the Logistics system is always an open complex systems. There are a number of factors including both qualitative factors and quantitative indicators impact of the changes in the demand. In addition, the development of the Logistics is always presented in a general non-linear and random state, and it is closely related to the level of economic development of the city, the government policies and other factors etc. These factors are not only difficult to describe quantitatively, but also difficult to establish the certain functional relation with the forecasted goal (2)-(3). So, it is not appropriate to use the aforementioned methods. The BP network based on gradient descend is a new
Jian-sheng Shao - One of the best experts on this subject based on the ideXlab platform.
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The forecast of Logistics Cost of coal enterprises based on PSO-BP
Journal of Liaoning Technical University, 2010Co-Authors: Jian-sheng ShaoAbstract:In order to forecast the Logistics Cost of coal enterprises effectively,the PSO-BP and BP network are respectively used to forecast the Logistics Cost ,the result shows that the PSO-BP network's convergence speed and prediction accuracy are obvious better than BP network. The PSO-BP neural network is very attractive for a wide application in forecasting Logistics Cost in coal enterprises.
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The PSO-BP-based forecast of Logistics Cost for coal enterprises
2010 IEEE 17Th International Conference on Industrial Engineering and Engineering Management, 2010Co-Authors: Jian-sheng ShaoAbstract:The forecast of Logistics Cost is the prerequisite and foundation for Logistics Cost management in coal enterprises. This paper has analyzed the Logistics Cost in coal enterprises and its influencing factors, using PSO-BP and BP network respectively to forecast the Logistics Cost in coal enterprises. As a result, it comes to a conclusion that the convergence speed and the predicted accuracy of PSO-BP network are obviously better than that of BP network.
Jaafar Pyeman - One of the best experts on this subject based on the ideXlab platform.
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Logistics Cost Accounting and Management in Malaysia: Current State and Challenge
International journal trade economics and finance, 2020Co-Authors: Sahidah Zakariah, Jaafar PyemanAbstract:Logistics Cost is an important factor affecting the competitiveness on both macro level (national) and micro level (firms). Logistics Cost indicates the performance of Logistics industry, efficiency level and its competitiveness. Despite of its significance, current state of Logistics Cost accounting and management in Malaysia has not properly addressed and the challenges surround Logistics Cost measurement remains incoherent. Therefore, this study aim to shed light on the current state and challenges of Logistics Cost accounting and management in Malaysia. This study conducted in two fold which are; 1) content analysis and, 2) survey analysis. Content analysis used to view current state and challenges in macro level with regards the concerned research, while survey analysis used to view current scenario in micro level. This study reveals three major challenges that become a barrier to fully understanding and implementing Logistics Cost accounting and management. First, there is no unified definition of Logistics Cost. Second, the measurement and Cost component included in the calculation of Logistics Cost are not standardized. Third, there is a difficulty in collecting information and data both in published sources and direct sources. This study conclude with the importance of having standard Logistics Cost accounting measurement, which plays a vital role in determining the accuracy of the Logistics Cost and ascertain the efficiency level of Logistics industry particularly in Malaysia
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Current State and Issues of Logistics Cost Accounting and Management in Malaysia
2020Co-Authors: Sahidah Zakariah, Jaafar PyemanAbstract:Background: Logistics Cost is an important factor affecting the competitiveness on both macro (national) and micro level (firms). Logistics Cost indicates the performance of Logistics industry, efficiency level and its competitiveness. Research Problem: Despite of its significance, current state of Logistics Cost accounting and management in Malaysia has not properly addressed and the issues surround Logistics Cost measurement remains incoherent. Aim of research: The purpose of this study is to give an overview of the current state and issues of Logistics Cost accounting and management in Malaysia. Research Method: This study used content analysis as a qualitative research tool, and supported by literature material with regards the concerned research tool. Findings: This study has found the importance of having standard Logistics Cost accounting measurement, which plays a vital role in determining the accuracy of the Logistics Cost and ascertain the efficiency level of Logistics industry in Malaysia. Implication: This study leads to trigger the awareness of current state and issues of Logistics Cost accounting and management in Malaysia.
Gao Jian - One of the best experts on this subject based on the ideXlab platform.
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Research on Enterprise Logistics Cost Control
Logistics Technology, 2020Co-Authors: Gao JianAbstract:Enterprise Logistics Cost control is systematically analysed in this paper and its theoretical framework is presented as well. The measures of Logistics Cost control should be focused on systematical and comprehensive control, moreover, computer network control systems should play sufficient roles in these procedures.
