PENGAMBILAN KEPUTUSAN DENGAN TEKNIK SOFT COMPUTING

[Id]Soft Computing adalah sebuah metode yang baik untuk melakukan pengolahan data. Teknik soft computing telah membawa kemampuan otomatisasi ke aplikasi tingkat baru. pengendalian proses adalah sebuah aplikasi penting dari industri apapun untuk mengendalikan parameter sistem yang kompleks, dengan pengendalian paramater dapat memberikan added value dari kemajuan tersebut. Pada pengendalian konvensional umumnya berdasarkan pada model matematika yang menggambarkan perilaku dinamis dari sistem pengendalian proses. Pada pengendalian konvensional terdapat kekurangan yang dapat dipahami, pengendali konvensional sering kalah dengan pengendali (controllers) cerdas. Teknik soft computing memberikan kemampuan untuk membuat keputusan dan belajar dari data yang dapat diandalkan. Selain itu, teknik soft computing dapat mengatasi dengan berbagai lingkungan dan stabilitas ketidakpastian. Makalah ini membahas berbagai bagian teknik soft computing yaitu. fuzzy logic, algoritma genetika dan hibridisasi dan meringkas hasil kasus pengendalian proses. Hasil kesimpulan diperoleh pengendali soft computing memberikan kontrol yang lebih baik pada kesalahan dibandingkan pengendali konvensional. Selanjutnya, pengendali algoritma genetika hibrida berhasil dioptimalkan.Kata kunci :Fuzzy logic, Algoritma Evolusioner, Algoritma Genetika, Turbin Compressor System[En]Soft Computing is a good method to perform data processing. Soft computing techniques have brought automation capabilities to a new level applications. process control is an important application of any industry to control the parameters of complex systems, the control parameters can provide the added value of such advances. In the conventional control is generally based on a mathematical model that describes the dynamic behavior of the process control system. In the conventional control there are deficiencies that can be understood, conventional controllers are often inferior to the controller intelligent. Soft computing techniques provide the ability to make decisions and learn from reliable data. In addition, soft computing techniques can cope with different environments and stability of uncertainty. This paper discusses the various parts of soft computing techniques viz. fuzzy logic, genetic algorithms and hybridization and summarizes the results of a case control process. The conclusion obtained by controlling soft computing provides better control on the error compared to conventional controllers. Furthermore, genetic algorithms hybrid controllers successfully optimized.Keywords: Fuzzy Logic, Evolutionary Algorithms, Genetic Algorithms, Turbine Compressor System

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Fuzzy Knowledge Based System for Suitability of Soils in Airfield Applications

Proper design of roads and airfield pavements requires an in-depth soil properties evaluation to determine suitability of soil. Soft computing is used to model soil classification system's dynamic behaviour and its properties. Soft computing is based on methods of machine learning, fuzzy logic and artificial neural networks, expert systems, genetic algorithms. Fuzzy system is a strong method for mimicking human thought and solves question of confusion. This paper proposes a new decision-making approach for soil suitability in airfield applications without a need to perform any manual works like use of tables or chart. A fuzzy knowledge - based approach is built to rate soil suitability in qualitative terms for airfield application. The proposed model describes a new technique by defining fuzzy descriptors using triangular functions considering the index properties of soils as input parameters and fuzzy rules are generated using fuzzy operators to classify soil and rate its suitability for airfield applications. The data obtained from the results of the laboratory test are validated with the results of the fuzzy knowledge-based system indicating the applicability of the Fuzzy model created. The approach developed in this work is more skilled to other prevailing optimization models. Due to its system’s flexibility, it can be suitably customized and applied to laboratory test data available, thus delivering a wide range for any geotechnical engineer. Doi: 10.28991/cej-2021-03091643 Full Text: PD

Recent Trends and Applications of Soft Computing: A Survey

Abstract: This paper is survey on the development of soft computing applications in various domains. Specifically, it briefly reviews main approaches of soft computing (in the wide sense) , the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. This paper presents applications of using different Soft Computation methods in both industrial, biological processes, in engineering design, in investment and financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad