Implementation and Development of Learning Vector Quantization Supervised Neural Network
نویسندگان
چکیده
Abstract Electricity is vital energy for the sustainability of human activities both as individuals, community groups, and industrial world. users are increasing every year, which causes irresponsible does not comply with existing rules; number staff to find it challenging determine whether power used appropriate household needs. This study uses data on 100 electricity obtained from PT. PLN Rayon Trade one branch offices in Indonesia. The method classify Learning Vector Quantization (LVQ) algorithm using 4-8-3 architectural model. Several input variables used, such bills, hours, metered rate, class. results an accuracy rate 72% a time 11 minutes 53 seconds. So can be concluded that LVQ model users. However, very good because still needs improved.
منابع مشابه
Supervised Neural Gas for Learning Vector Quantization
In this contribution we combine approaches the generalized leraning vector quantization (GLVQ) with the neighborhood orientented learning in the neural gas network (NG). In this way we obtain a supervised version of the NG what we call supervised NG (SNG). We show that the SNG is more robust than the GLVQ because the neighborhood learning avoids numerically instabilities as it may occur for com...
متن کاملSupervised Neural Gas and Relevance Learning in Learning Vector Quantization
Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype—based clustering algorithm.Generalized relevance LVQ (GRLVQ) constitutes a modification which obeys the dynamics of a gradient descent and allows an adaptive metric utilizing relevance factors for the input dimensions. As iterative algorithms with local learning rules, LVQ and m...
متن کاملFast sequential implementation of "neural-gas" network for vector quantization
Although the “neural-gas” network proposed by Martinetz et al. in 1993 has been proven for its optimality in vector quantizer design and has been demonstrated to have good performance in time-series prediction, its high computational complexity (N logN) makes it a slow sequential algorithm. In this letter, we suggest two ideas to speedup its sequential realization: 1) using a truncated exponent...
متن کاملVector Quantization and Projection Neural Network
Classical data analysis techniques are generally linear. They fail to reduce the dimension of data sets where dependence between observed variables is non-linear. However, for numerous scientific, industrial and economic areas, it should be desirable to obtain a low-dimensional parametric representation of the data set. Model fitting is a way to obtain a usable representation of an observed phe...
متن کاملBorder sensitive fuzzy vector quantization in semi-supervised learning
Abstract. We propose a semi-supervised fuzzy vector quantization method for the classification of incompletely labeled data. Since information contained within the structure of the data set should not be neglected, our method considers the whole data set during the learning process. In difference to known methods our approach uses neighborhood cooperativeness for stable prototype learning known...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of physics
سال: 2022
ISSN: ['0022-3700', '1747-3721', '0368-3508', '1747-3713']
DOI: https://doi.org/10.1088/1742-6596/2394/1/012009