РАЗРАБОТКА МЕТОДИКИ МОДЕРНИЗАЦИИ МАРШРУТА ТЕХНИЧЕСКОГО ДОКУМЕНТООБОРОТА НАУКОЕМКОГО ПРОИЗВОДСТВА

An artificial neural network (ANN) representation method is presented and discussed in this paper. ANN representation of symbolic and numeric knowledge is realized by the improved backpropagation (BP) algorithm. The method of management and organization of ANN representation is presented also. The problem that numeric knowledge is difficult to be represented is effectively solved by the network. After learning, the threshold values and weights of ANN are ensured and the diagnostic knowledge is learned. The practical examples are used to check the diagnostic results. It is shown that the ANN representation method is effective.

The article is devoted to considering the use of the decision tree method, defects at a machine-building enterprise, the reasons for having defects in the product and proposals for amending the normative and technical documentation. At the moment, non-destructive diagnostics methods have become widespread, which make it possible to determine the places of fault localization and predict the object state without the need for research which requires making object inoperative or its dismantling. The construction of the diagnostic models is implemented using the methods and tools of regression analysis, the theory of artificial neural networks, etc. However, they do not have a high level of generalization, and neural networks are difficult to interpret, which complicates their application in practice.

The objective of this research is to implement a method for estimating the real missing data in heart disease datasets and to show how it affects the resulting knowledge. Missing data is common problem in knowledge discovery from database (KDD) processes that can lead significant error in extracted knowledge. We use hybridization of artificial neural network and rough set theory (ANNRST) to estimate the real missing data on heart disease from UCI (University of California, Irvine) datasets. ANN with reduced input features is used to estimate the missing data. RST is used to reduce the dimensionality of input features and to extract the knowledge as reducts and rules from heart disease datasets with estimated missing data. RST, decomposition tree, local transfer function classifier (LTF-C) and k-nearest neighbor (k-NN) classifier are used to calculate the accuracy. Comparative study with k-NN estimation, most common attribute value filling and deletion of missing data are made to evaluate the extracted knowledge. ANNRST can be considered as the appropriate estimation method when strong relationship between original complete datasets and estimated datasets is important (the estimated datasets really represent the nature of original complete datasets) as it gives the best accuracy and coverage for almost all the classifiers.

Introduces the theory of artificial neural networks (ANN). Discusses its application to the valuation of residential apartments. Compares the performance of the back propagation neural network (BP) model in estimating sale prices of apartments against the traditional multiple regression analysis (MRA) model. Concludes that the neural network model is an easy‐to‐use, black‐box alternative to the MRA model.

Archiving research of scientific and technical documentation is traditionally is considered in scientific and methodological works, as well as training courses as an equal part of general archival studies. But at the same time, the definition of the term “scientific and technical documentation” has not been fixed yet in the regulatory documents, in historiography, as well as in the terminological standards, where we observe different interpretations of this term. Obviously, the main issue in defining the term “scientific and technical documentation” is its cross-system nature, and likewise its polysemantic use in various fields of science. It is typical that different methodological documents and draft regulations include different documentation systems in this concept. At the same time, the terms “scientific and technical document”, “electronic scientific and technical document” are now actively used. The article analyzes the terminological controversies of the archival studies of scientific and technical documentation. The main issue considered by the author is the definition of the terms “scientific and technical documentation” and “electronic scientific and technical document”. The paper scrutinizes the history of the use of the terms, their historiography. The issues of the relationship between management documentation and scientific and technical documentation are also touched upon. The author of the article formulates and justifies the definitions of the terms “scientific and technical documentation”, “scientific and technical document”, “electronic scientific and technical document”.

In this work we sketch a new theory of artificial neural network, based on a paraconsistent annotated logic Eτ. Such theory, called Paraconsistent Artificial Neural Network – PANN – is built from the algorithm Para-analyzer and has as characteristics the capability of mainly manipulating uncertainty, inconsistent and paracomplete concepts. Some aspects such as capability of adaptation, velocity processing, and other useful characteristics make the PANN a promising theory.

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

New global exponential stability criteria for nonlinear delay differential systems with applications to BAM neural networks

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

The article describes a two-level model of distribution of preferences, developed by the authors. The main provisions of subjective analysis and theory of artificial neural networks. We describe the application of the modeling for consideration internally personal conflict

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

Technical documentation differs from normal documentation as created by ordinary text processing systems in the following areas: Technical documents are often very large in size. Documents exceeding 1000 pages are no exception. Technical documents are created by a group of authors often working concurrently. Technical documents that describe a product have a long lifetime, which follows the progressive development of the product described. This development has to be supported by multiple versions of one document. Technical documents have to incorporate information from other sources (other documents, but also paper and remote computer systems). Since technical documents are generally complex, there is a need for management support for such documents. Since technical products are often designed as a series, e.g. configurations which differ in details only, the various documents describing the products also exist as close variants of each other.

The theory of classical artificial neural networks has been used to solve pattern recognition problems in image processing that is different from traditional pattern recognition approaches. In standard neural network theory, the first step in performing a neural network calculation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for non-linearity of the network. This paper presents the fundamental theory for a morphological neural network which, instead of multiplication and summation, uses the non-linear operation of addition and maximum. Several basic applications which are distinctly different from pattern recognition techniques are given, including a net which performs a sieving algorithm.

We provide the conditions for the existence of a periodic solution in two-dimensional systems of ordinary differential equations, which arise in the theory of genetic and artificial neural networks. The proof is based on Poincare-Andronov-Hopf bifurcation. Multidimensional attractors can be constructed using the two-dimensional ones. Illustrations and examples are provided.