Training sample reduction based on association rules for neuro-fuzzy networks synthesis

Abstract

The problem of reduction of training samples for synthesizing diagnostic models has been solved in the paper. The method of dimension reduction of training sample based on association rules has been proposed. It includes the implementation of stages of reduction of instances, features and superfluous terms, uses information on extracted association rules for evaluation of informativeness of features. The proposed method allows to create a partition of feature space with a fewer number of instances compared to the original sample, which in turn makes the synthesis of easier and more con venient for perception diagnostic models possible. The developed method has been implemented in the developed software and was used for the practical problem solving of reduction of training sample for the synthesis of a diagnostic model of confectionery products quality.

Construction of a genetic method to forecast the population health indicators based on neural network models

“…where β 1 , β 2 are the hyperparameters indicating the exponential rate of decay at the time of evaluation; η is the initial level of training; ε is the small constant, introduced for numerical stability; m ω is the exponential movable mean of the gradient; v ω is the exponential mean of gradient square; ∇ ω L (t) is the gradient value over time t; ω is the vector of gradient descent parameters [23]. Typically, the architecture of a neural network model, its topology, and the values of macro parameters are chosen based on an expert evaluation or empirically.…”

Development of the method for decomposition of superpositions of unknown pulsed signals using the second­order adaptive spectral analysis

“…In addition, we can use such methods for segmentation of images [20], improvement of blurred images [21], processing of x-rays [22] and low contrast [23,24]. Papers [25][26][27][28][29][30] present methods for modeling complex dependences based on computational intelligence [25], associative rules [26], negative selection [27], neural-fuzzy networks [28], agent technologies [29], stochastic search [30]. The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27].…”

“…Papers [25][26][27][28][29][30] present methods for modeling complex dependences based on computational intelligence [25], associative rules [26], negative selection [27], neural-fuzzy networks [28], agent technologies [29], stochastic search [30]. The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27]. However, the methods proposed in papers [25][26][27][28][29][30] do not allow solving the problems associated with processing data presented in the form of time series effectively.…”

“…The methods proposed in [25][26][27][28][29][30] make it possible to process data presented in various formats efficiently: usual samples of multidimensional data [25,[28][29][30], transaction databases [26], samples containing missing values [26,27]. However, the methods proposed in papers [25][26][27][28][29][30] do not allow solving the problems associated with processing data presented in the form of time series effectively. Articles [31][32][33] propose information technologies, which realize methods [25][26][27][28][29][30].…”

“…However, the methods proposed in papers [25][26][27][28][29][30] do not allow solving the problems associated with processing data presented in the form of time series effectively. Articles [31][32][33] propose information technologies, which realize methods [25][26][27][28][29][30]. Despite high efficiency in processing of large-volume of multidimensional data, such methods do not solve the problems of signal processing and time series [34] effectively enough.…”