Search results
Jul 20, 2021 · soft computing; decision-making; fuzzy sets; fuzzy environment; Dzitac. 1. Introduction. This paper is dedicated to Professor Ioan Dzitac (14 February 1953–6 February 2021). Therefore the paper begins with a short presentation of his life and work. Ioan Dzitac was born in the village of Poienile de sub Munte, in the County of Maramures ...
Soft computing is the subject of both theoretical and practical research, and soft computing techniques are currently being applied in many applications in areas such as industrial systems, commercial, or domestic applications. This Topic is open to receive submissions of high-quality papers regarding advances in soft computing and its ...
Aug 7, 2022 · The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work ...
Jan 12, 2020 · Solar photovoltaic (PV) systems are attracting a huge focus in the current energy scenario. Various maximum power point tracking (MPPT) methods are used in solar PV systems in order to achieve maximum power. In this article, a clear analysis of conventional MPPT techniques such as variable step size perturb and observe (VSS-P&O), modified incremental conductance (MIC), fractional open circuit voltage (FOCV) has been carried out. In addition, the soft computing MPPT techniques such as fixed ...
Aug 14, 2023 · With the development of soft computing techniques, many scholars have tried to develop these models for solving science and engineering problems [2,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. These techniques such as artificial neural networks (ANN), fuzzy neural inference system (Neuro-Fuzzy), and the support vector machine (SVM) have also been proposed to predict EISL using in situ test data.
Dec 1, 2021 · In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause ...
Soft computing techniques, such as neural networks and fuzzy sets, are able to represent the foundations of decision-making processes, and hence, can solve general decision problems. Neural networks provide powerful architectures for solving complex problems, while fuzzy sets offer general means for preference modeling under uncertainty.
May 30, 2023 · Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LVQ), were employed to classify four samples of fake saffron (dyed citrus blossom, safflower, dyed fibers, and mixed stigma with stamens) and three samples of genuine saffron (dried by different ...
Jun 23, 2023 · Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive information about the load weight indirectly from other sensors. In this paper, a static payload weight is estimated based on the local strain of a crane’s girder and the current position of the trolley ...
Jan 15, 2021 · Soft-Computing techniques applied to energy-related problems usually face data-driven tasks, such as optimization, classification, clustering or prediction problems, among others. In many cases, these problems are in close connection with alternative applications such as Renewable Energy resource evaluation, design of energy efficiency systems, or very different energy system applications in smart grids, etc.
