A case-based knowledge system for safety evaluation decision making of thermal power plants

Gu, Dongxiao; Liang, Changyong; Bichindaritz, Isabelle; Zuo, Chunrong; Wang, Jun

doi:10.1016/j.knosys.2011.08.002

Cited by 41 publications

(22 citation statements)

References 57 publications

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“…Several taxonomies and concepts of uncertainty have been proposed in recent years (e.g., linguistic uncertainty, knowledge/epistemic uncertainty, variability/aleatoric uncertainty, decision uncertainty, procedural uncertainty, etc.). Gu et al [166] identified four interrelated categories of uncertain information:…”

Section: S4 Benefits and Outcomesmentioning

confidence: 99%

“…Accordingly, integrating the active parameter (Uncertainty handling) in our proposed classification indicates if a selected paper tried to propose an approach to handle one or more specific type of uncertainty or not. Thus, the Uncertainty type(s) (C1) is firstly identified according to the above-mentioned four categories [166]. Then, the proposed Uncertainty solution(s) (C2) to deal with each type of uncertainty is identified.…”

Section: S4 Benefits and Outcomesmentioning

confidence: 99%

See 1 more Smart Citation

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

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Achieving sustainable energy planning and development involves complex decision-making processes. The energy planning decision-making (EPDM) field relies on a plethora of decision analysis methods that offered many solutions to process a variety of energy management and strategic decision-making problems. However, current EPDM solutions are unable to overcome the increasing complexity of strategic energy planning situations involving a large number of stakeholders in uncertain, dynamic, and distributed environments. This raises significant new challenges for researchers in both decision sciences and renewable and sustainable energy planning. On the basis of a representative assortment of peer-reviewed related literature selected by querying multiple electronic databases and indexed in Scopus and Web of Science databases domain journals over the last 12 years, this paper exhaustively highlights and discusses limitations of existing strategic EPDM solutions. The analysis is based on a classification specially developed by holistically harmonizing important domain concepts to categorize the considered representative sample of the field of interest. Additionally, this paper integrates results and conclusions from some recent and most cited literature reviews to (i) formulate essential evidence as well as practical and conclusive literature's supportalongside with the formulated representative sample -to this paper's subsequent insights and statements, and (ii) guarantee that no relevant articles have been excluded. A total of 78 related works is gathered and analyzed to provide a general view and discussion on major complexities found in classical/traditional strategic EPDM solutions and challenges for next-generation EPDM solutions. Moreover, a comparative analysis of the two solutions and a set of "quality indexes" of a next-generation EPDM solution were identified and some proposals were made to improve future applicative research. As an original result coming from the "quality indexes" identified through the review process, an intelligent expert-based framework for next-generation EPDM solutions is developed for enhanced renewable and sustainable energy planning.

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Section: S4 Benefits and Outcomesmentioning

confidence: 99%

Section: S4 Benefits and Outcomesmentioning

confidence: 99%

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

show abstract

“…In order to better solve the problems of determine the initial weights and threshold, can adopt the weights and threshold based on genetic algorithm, using the global search ability of genetic algorithm to determine the initial weights and threshold.This paper adopts three layer BP network to determine the initial solution space, setting training number and training error of the network. Set the input population size, crossover probability ( c P ), mutation probability ( m P ), the network layers, each layer neural metadata, and use GA to optimize the weights of neural network repeatedly, until the average value is no longer meaningful increase so far, at this time the decoded parameter combination has sufficiently close to the optimum combination of parameters, and then BP algorithm reoptimization connection weights and threshold of the network in the small solution space , search out the optimal solution [4]. Because GA is based on the population, not to search base on a single point , can also obtain a plurality of extreme value from different points, so it is not easy to fall into local optimum, which can effectively solve the existing problem in BP neural network, and effectively improve the generalization performance of neural network.…”

Section: Neural Networkmentioning

confidence: 99%

Enterprise Credit Evaluation Model Based on Genetic Algorithm Optimization BP Neural Network

Zhou¹,

Qian²,

Jiang³

et al. 2015

Advances in Intelligent Systems Research

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Abstract. Credit is very important for the enterprise, analysis of a comprehensive evaluation of enterprise credit, can enhance the risk control ability of enterprise credit, improve enterprise credit rating.The paper establishes BP neural network credit evaluation model based on genetic algorithm （GA）optimization ,and to the power supply enterprise credit evaluation as an example, to verify the practicability of the model in the evaluation of enterprise credit. Examples of verification results indicate that the genetic algorithm (GA) to optimize the BP neural network is better than traditional BP neural network , it's evaluation has higher accuracy, stronger generalization ability, more suitable for the enterprise credit evaluation.

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“…Well-done intelligent fault identification methods have been presented previously [19][20][21][22][23]. However, decision tree and PCA (principal component analysis)-based method [19] was hard to predict the value of continuous attribute (needed discretization first).…”

Section: Introductionmentioning

confidence: 99%

A rule-based intelligent method for fault diagnosis of rotating machinery

Dou

Yang

Liu

et al. 2012

Knowledge-Based Systems

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A case-based knowledge system for safety evaluation decision making of thermal power plants

Cited by 41 publications

References 57 publications

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Enterprise Credit Evaluation Model Based on Genetic Algorithm Optimization BP Neural Network

A rule-based intelligent method for fault diagnosis of rotating machinery

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