Business performance forecasting of convenience store based on enhanced fuzzy neural network

Li, S. G.; Wu, Zhengtao

doi:10.1007/s00521-007-0158-y

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…The optimization heuristic used in [18] to obtain good schedule is genetic algorithm. The basic idea of genetic algorithms is given in Figure 1 [20]. In genetic algorithm we take initial population and then shortlist some parents from that population.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

Chauhan

Nitin

2014

Journal of Engineering

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Complex problems consisting of interdependent subtasks are represented by a direct acyclic graph (DAG). Subtasks of this DAG are scheduled by the scheduler on various grid resources. Scheduling algorithms for grid strive to optimize the schedule. Nowadays a lot of grid resources are attached by P2P approach. Grid systems and P2P model both are newfangled distributed computing approaches. Combining P2P model and grid systems we get P2P grid systems. P2P grid systems require fully decentralized scheduling algorithm, which can schedule interreliant subtasks among nonuniform computational resources. Absence of central scheduler caused the need for decentralized scheduling algorithm. In this paper we have proposed scheduling algorithm which not only is fruitful in optimizing schedule but also does so in fully decentralized fashion. Hence, this unconventional approach suits well for P2P grid systems. Moreover, this algorithm takes accurate scheduling decisions depending on both computation cost and communication cost associated with DAG's subtasks.

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Section: Related Workmentioning

confidence: 99%

Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

Chauhan

Nitin

2014

Journal of Engineering

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“…Holistic overview of the current literature indicates that profit forecasting methods are mainly divided into two categories. One is statistical methods, such as regression model (RM), time series analysis, and gray system theory, another is neural network (NN) method[ 11 ][ 12 ]. However, little attention has been directed to network graph (NG) method.…”

Section: Introductionmentioning

confidence: 99%

The improved business valuation model for RFID company based on the community mining method

2017

PLoS ONE

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Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company’s net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies.

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“…Some scientists have claimed that there is no fuzzy concept in the world. For example, Rudolf E. Kálmán stated in 1972 that fuzzy concepts did not exist since they were not scientific concepts [11,12]. This suggestion was based on an idea that a scientific concept should be clear and precise.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Case-Based Reasoning System

Bai

Zhang

et al. 2016

Applied Sciences

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Abstract:In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the "vague" concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.

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Business performance forecasting of convenience store based on enhanced fuzzy neural network

Cited by 9 publications

References 19 publications

Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

Decentralized Scheduling Algorithm for DAG Based Tasks on P2P Grid

The improved business valuation model for RFID company based on the community mining method

Fuzzy Case-Based Reasoning System

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