The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the case of heterogeneous objects and situations with many different types of parameters and their possible uncertainty. In this paper, an approach based on machine (deep) learning is investigated for this task. It is proposed to carry out the process of selecting situations and solutions from the knowledge base in two stages: recognition of the states of the elements of a complex object and the relationships between them, then the formation of a representation of the situation in the state space and its use for comparing situations using neural networks. An ensemble neural network model based on a multi-layer network is proposed. It successfully simulates the cognitive functions of a human (expert), correctly selects similar situations and ranks them according to the similarity parameter. Proposed neural network models provide the implementation of a hybrid-CBR approach for decision-making on complex objects.
Currently, companies are consuming transitions to the development of the difficult oil and gas fields. The difficulty implies factors: features of geological conditions, remote geographic location, features of the relief. The development of new oil and gas fields requires design approaches that ensure maximum profitability on complex assets. One of the promising development options is the digitalization and automation of design processes. The paper proposes a new approach to assessing capital costs when designing well pads in the field. A new method is proposed for calculating costs and restrictions at the stage of resources for optimizing a well pad, taking into account detailed topography and resource availability through digitalization and automation. The problem was solved using an interactive ontological model with built-in knowledge bases and calculation algorithms. The model was tested at the field, the possible risks of using the model were assessed, and sufficient accuracy of the obtained values was obtained. The results of the work make it possible to improve the stage of optimization of the well pad, taking into account the costs of resources: drilling, engineering preparation, backfilling of the road, supply of communications, availability of resources and unforeseen costs. The work supports the trends of digitalization and technological processes and business processes. The developed model made it possible to digitize the stage of optimizing the location of the well pad, to automate the multifactor calculation of costs and restrictions. The results make the possible full automation for definition well pad placement, later on, taking into account detailed topography and resource availability.
Выходные данные статьи и справка о публикации: в день оплаты статьи Рассылка печатных журналов и оттисков: 05.10.2022
The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.
Smart grid systems are being actively developed and implemented all over the world. However, along with developed systems for monitoring and data analysis, decision support functions are not fully implemented. Wherein decision support is necessary due to the complexity of possible emergencies. In this work, we offer the concept of an intelligent decision support system (IDSS) for the SMART grid, which is based on the hybrid Case-Based Reasoning (CBR) method. This method combines models of knowledge-based systems and models of neural networks and machine learning, which simplifies realization on complex changing objects of the SMART grid. In the first part of the research, we describe the concept of the proposed hybrid-CBR method, the principle of formalizing the situation at the objects of the SMART grid systems and present the involved neural network architecture Comparator-Adder. The second parts of the research reveal the concept of applied IDSS and also show the results of an experiment of retrieving precedent from a knowledge base with using a neural network. Experimental results show that our architecture successfully copes with the task of selecting the most similar situation. We believe that the MAPE error in this incident does not play a key role; the efficiency of the neural network is confirmed primarily by the coherence with the results of the expert choice and the absence of collisions.
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