Optimization of learning the neuronetworking data processing system for non-satinary objects recognition and forecasting

Djumanov, Olimjan; Kholmonov, Sunatillo Makhmudovich

doi:10.1109/icaict.2010.5612037

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…indicator of the reliability of data processing. Implemented are typical computational schemes based on the use of the properties of fuzzy sets, fuzzy logic, NN, which use tools for calculating estimates of information content, the importance of heterogeneous and heterogeneous features of the objects under study [36][37][38][39]. A technique has been developed that contributes to the assessment of the degree of relatedness, mutual equivalence, equivalence, decorrelation, destructurization of RTS elements and optimization of RTS identification.…”

Section: Mechanisms For Optimizing the Identification Of Rts In A Fuz...mentioning

confidence: 99%

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Jumanov

Kholmonov²,

Djumanov³

2023

E3S Web Conf.

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Researched and developed mechanisms for optimizing the identification of random time series based on mechanisms for searching for unknown knowledge, hidden properties, patterns, relationships, features of non-stationary objects under the condition of limited a priori information, uncertainty, non-stationary processes. A generalized model for optimizing the identification of RTS based on the use of neural networks, neuro-fuzzy networks of dynamic models, as well as fuzzy logic algorithms is implemented. Instruments for data reliability control are obtained based on statistical, dynamic, intelligent approaches suitable for the conditions of transmission of incomplete, heterogeneous, partially given information with large parametric uncertainty. The principles and methods of data reliability control in a fuzzy environment are developed based on the generalization and use of the properties of neural networks, fuzzy logic and statistical modeling methods. Algorithms for searching for correlations, trends, interrelations and regularities in data, forming training, control and test sets for solving problems of recognition, classifying images of micro-objects and predicting power supply indicators are synthesized. The results of the research are implemented in the form of a software package for identifying non-stationary objects, which ensures the adaptability of model variables, high data processing performance and accuracy of results.

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Section: Mechanisms For Optimizing the Identification Of Rts In A Fuz...mentioning

confidence: 99%

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Jumanov

Kholmonov²,

Djumanov³

2023

E3S Web Conf.

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show abstract

“…Existing technologies for solving problems of identification, search for extrema, optimization, are determined by labor-intensive calculations and the use of highly iterative algorithms. In addition, they are characterized by insufficient generalization, the use of properties of non-stationary objects, models, statistical parameters, dynamic, specific characteristics [5,6,7,8]. It is necessary to research and develop a wide range of identification algorithms that contribute to the achievement of a higher quality of information processing in conditions of insufficient a priori information, parametric uncertainty, non-stationary and low data reliability [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Non-Stationary Objects Based on Training a Neural Network and Tuning the Parameters of a Generalized Model

Jumanov,

Kholmonov

2023

E3S Web Conf.

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Methods and algorithms have been developed for identifying non-stationary objects of various types using statistical, dynamic, neural network models, which are taken into account when solving problems of conditions of a priori insufficiency, uncertainty, low reliability of data. Mechanisms are proposed that provide effective identification based on combining the features of dynamic models with the properties of random time series. The possibilities of algorithms based on mechanisms that use statistical, dynamic, specific data characteristics, as well as the properties of self-adaptation, approximation, organization, self-learning of neural networks have been expanded. A generalized function identification algorithm has been developed and its functions have been expanded by adaptive segmentation of time series, setting the informative interval of element values, the size of the training set, training multilayer neural networks, database, and knowledge base. The training algorithms for a three-layer neural network are modified based on the mechanisms for regulating interneuronal connections in layers, weight coefficients of neurons, variable activation functions, network architecture, and superposition of continuous input-output dependencies. A software package for identifying random time series in the C++ language in the CUDA parallel computing environment has been developed to predict the annual power consumption of the industrial zone of the Samarkand region using software tools for data preprocessing, filtering, smoothing; determining the boundaries of the informative interval of time series elements.

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Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

Jumanov

Kholmonov

2022

J. Phys.: Conf. Ser.

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Scientific and methodological foundations have been developed for the optimal identification of non-stationary objects transmitted by random time series in control systems of industrial and technological complexes, monitoring of environmental protection and ecology, palynology, medicine based on the use of statistical, dynamic, neural network models and mechanisms for extracting statistical, dynamic specific characteristics of information. The mechanisms for controlling the error in the identification of RTS, based on the use of statistical criteria - the rules of the sequential analysis, Bayesian estimation, ±3σ, confidence interval, have been investigated. The problem of control of the error in the identification of RTS by the confidence interval, the mechanisms of threshold control of the values of the random time series elements, their increments, the error of prediction by statistical, trend, dynamic models, algebraic polynomials, parabolic, cubic interpolation, extrapolation spline functions. Mechanisms for regulating the variables of the random time series identification models based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed. The methodology for assessing the quality of the identification of RTS by mathematical expectation and variance and the criterion of correspondence between the relations of mathematical expectation and variance of two consecutive elements have been implemented. The experimental study was carried out according to the real data of the power grid enterprise. The effectiveness of the implemented mechanisms was studied according to the criteria of the minimum root-mean-square error, labor intensity, cost of information processing.

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Optimization of learning the neuronetworking data processing system for non-satinary objects recognition and forecasting

Cited by 3 publications

References 2 publications

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Identification of Non-Stationary Objects Based on Training a Neural Network and Tuning the Parameters of a Generalized Model

Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

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