Self-learning Procedures for a Kernel Fuzzy Clustering System

Hu, Zhengbing; Bodyanskiy, Yevgeniy; Tyshchenko, Oleksii K.

doi:10.1007/978-3-319-91008-6_49

Cited by 5 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reveal this uncertainty, information technologies to support artificial intelligence are used, which implement "soft computing" using neural networks, genetic algorithms, fuzzy sets, clustering [8]. The greatest effect is achieved when using hybrid technologies that use a combination of the above models and methods [9].…”

mentioning

confidence: 99%

Market Segmentation of Innovative Products Using Genetic Algoritms

Zatsarynin

2021

MDT

View full text Add to dashboard Cite

One way to increase the company's competitiveness is to find new market niches. The market niche is the result of innovations that stimulate hidden, potential demand, as a result of which the company, developing a new market, avoids intense competition and receives a higher rate of return. It is proved that the growing number and complexity of tasks in the field of marketing research, working with a large amount of information, leads to the need to group data. The aim of the study is to develop a universal approach to solving the problem of market segmentation of innovative products based on a combination of genetic algorithm with traditional clustering methods. An ideal market niche can be defined as a compact and isolated series of points, which in some space of characteristics are objects or data elements. The selection of a market niche in the medical equipment market is carried out using a top-down approach. This approach implies the traditional segmentation of customers, which is carried out in the following order: segmentation, segment selection, positioning. It is believed that segmentation is the starting point for the formation of a market niche. To segment the medical equipment market, it is proposed to use cluster analysis methods. According to the results of the analysis, it can be seen that the market segments of potential consumers of medical equipment and consumables of Siemens in Ukraine are characterized by a fairly dense grouping of images of consumers around the center of its cluster in the space of features. The presented genetic clustering algorithm is flexible in relation to the decision-making process, as it allows to perform clustering based on various criteria, such as maximum mutual removal of clusters, proximity of geometric images of objects to the center of the cluster, other criteria. This is achieved by changing the calculation formula of the fitness function, which takes into account the necessary combination of clustering criteria without changing the structure of the algorithm. The algorithm is insensitive to initialization, as in the process of evolution of chromosomes through the use of genetic operators, the algorithm completely covers the whole set of acceptable solutions, which, in turn, provides high quality market segmentation.

show abstract

mentioning

confidence: 99%

Market Segmentation of Innovative Products Using Genetic Algoritms

Zatsarynin

2021

MDT

View full text Add to dashboard Cite

show abstract

“…The run time of the algorithm is the number of steps that are performed in this algorithm from the beginning to the end. To count the number of steps investigated in this work algorithms, it is convenient to use the above block diagram [2], [4], [5], [8], [9], [10], [12], [13], [23], [24].…”

mentioning

confidence: 99%

“…It should be noted that in case of complication of the network structure, the magnitude of the weight vector can become a factor that will have a significant impact on the learning time of ANN [2], [10]. In general, we can conclude that the optimal choice for ANN training in the problem of identification of rheological parameters of wood is the algorithm of back propagation of the error in view of the optimal ratio of learning time and accuracy.…”

mentioning

confidence: 99%

Neural Network Model for Identification of Material Creep Curves Using Cuda Technologies

Sokolovskyy¹,

Shymanskyi²,

Mokrytska³

et al. 2019

UJIT

View full text Add to dashboard Cite

This paper addresses the problem of identifying rheological parameters of wood using artificial neural networks with parallel learning algorithm using Python programming language, Chainer framework and CUDA technology. An intelligent system for identification of rheological parameters of wood has been developed. The system created contains the most user-friendly interface, all the necessary set of tools for automation of the process of visualization and analysis of data. In the process of creation of the intellectual system, the following tasks were envisaged: to carry out the analysis of artificial intelligence systems and the analysis of training of artificial neural networks, in particular multilayer neural networks of direct propagation, recurrent neural networks and the Kohonen neural network; examine the structure of the Chainer framework and its interaction with CUDA; to conduct existing cloud technologies to accomplish the task; to conduct the analysis of algorithms of studies of artificial neuron networks, their mathematical providing; to implement parallelization of learning algorithms and to develop the necessary software. Using Chainer allows you to create a memory pool for GPU memory allocation. To avoid memory allocation and erasure during computing, Chainer provides the ability to use the CuPy memory pool as a standard memory allocation without dealing with memory allocation. An intellectual system to determine the physical and mechanical parameters of a mathematical model of non-isothermal moisture transfer and viscoelastic deformation of capillary-porous materials was developed. It provides the opportunity to identify parameters of the kernels of creep and relaxation that is written as a linear combination of exponential operators. The proposed algorithm of approximation and obtained calculated ratios of rheological behavior of wood by means of multilayer neural network with exponential activation functions in hidden layers allows to increase the accuracy of approximation of experimental creep data. The developed mathematical models can be used to create an automated systems of finite-difference calculation of temperature and moisture content, stress components during the drying of capillary-porous materials with taking into account the technological parameters of the drying agent.

show abstract