Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

Tkachenko, Roman; Izonin, Ivan; Tkachenko, Pavlo

doi:10.1007/978-3-030-82014-5_47

Cited by 6 publications

(4 citation statements)

References 19 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A new neuro-fuzzy diagnostic system was presented in [23] by combining a non-iterative ANN with a novel fuzzy model, the T-controller. Using a Padé polynomial for improved accuracy and coefficients synthesized using an optimized simulated annealing simulation on a pre-trained SGTM neurallike structure, the system has been tested on actual data to forecast generator power from 13 variables.…”

Section: Related Workmentioning

confidence: 99%

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Anbazhagu,

Koti,

Muthukumaran

et al. 2023

DGAEJ

View full text Add to dashboard Cite

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Anbazhagu,

Koti,

Muthukumaran

et al. 2023

DGAEJ

View full text Add to dashboard Cite

show abstract

“…Головною їх відмінністю від інших методів є те, що нейромережі не потребують заздалегідь відомої моделі, а будують її самі лише на основі інформації, яку отримали. Саме тому нейронні мережі увійшли до практики усюди, де потрібно вирішувати завдання прогнозування, класифікації, управління -іншими словами, в галузі людської діяльності, де є завдання, що погано алгоритмізуються, для вирішення яких необхідні або постійна робота групи кваліфікованих експертів, або адаптивні системи інформатизації, якими і є нейронні мережі в широкому розумінні [1; 2; 3; 4] [10].…”

Section: Abstract: Neural Network Machine Learning Economic Indicator...unclassified

Problems of Retro-Forecasts of Economic Indicators Based on Neural Networks

RIPPA

2022

WOF

View full text Add to dashboard Cite

Introduction. TThe difficult economic situation in Ukraine, the state of emergency, the war and the consequences of the destruction of critical infrastructure have significantly increased the value of economic and mathematical forecasting tools based on neural networks and their tuning capabilities, is improved. The purpose of the article is to analyze and study the potential and formal aspects of the application of neurocomputer methods of economic forecasting and tools to support retro-forecasts of economic indicators. Results. The success of economic decisions (strategic and tactical) in one way or another depends on the quality of analytics and the efficiency of the operational apparatus of decision-making. Even if a rigorous algorithmic approach is difficult or impossible and it is fundamentally impossible to get the right solution, there are effective methods and tools for solving economic problems, an important place among which is forecasting. Just in recent years there has been a breakthrough in the theory and practice of economics and mathematics, many organizations have begun to actively use neural networks in forecasting. Such neural networks can identify patterns by which they generate recommendations for action, they can study and summarize past experiences to improve their own level of performance and calculate forecasts. Neural network methodologies in general and neuro-forecasting in particular belong to the family of machine learning technologies. The specifics of solving the problem of machine learning in forecasting differs from other methods designed for the formation and use of predictions. Possibilities of accumulation and improvement of experience, formation and adaptation of neural network architecture to specifics of forecasting tasks, wide possibilities of application of retro-forecasting methods for improvement of characteristics of adjustment of neural models for forecasts, availability of flexible mechanisms of parameterization and optimization of algorithmic providing forecasting in economic research. Machine learning today is a field of scientific knowledge that is developing rapidly and deals with algorithms capable of learning and developing, which distinguishes this field of research from many others and makes it, at the same time focused on implementation practice, including forecasting. The need to use machine learning methods is due to the fact that for many complex – “intelligent” – tasks (eg, construction and improvement of predictive models) is very difficult (or even impossible) to develop an “explicit” algorithm for solving them, but often you can teach a computer to learn solving these problems with the help of neural networks and retro-forecasting technologies. Conclusions. The formalization of the integrated representation of the neural network for forecasting in the form of activation functions with the definition of conditions for their use in machine learning algorithms, taking into account the specifics of the refined settings of retro-predicted neural models of economic indicators. Demonstrated by the example of retro-forecasting for the simplest neural network (4-2-3-1), when the choice of machine learning algorithm (between PROP and RPROP) is achieved more than twice the effect of improving the quality of the forecast model.

show abstract

“…There are many clustering methods, such as DPC [18], DBSCAN [19], spectral clustering [20]. In addition to those aforementioned clustering algorithms, the non-iterative ANN can also be used to solve the clustering problem [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

Fan

Wang

et al. 2022

Mathematics

View full text Add to dashboard Cite

The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.

show abstract

Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller

Cited by 6 publications

References 19 publications

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Problems of Retro-Forecasts of Economic Indicators Based on Neural Networks

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

Contact Info

Product

Resources

About