scholarly journals PENGAMBILAN KEPUTUSAN DENGAN TEKNIK SOFT COMPUTING

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Nopi Ramsari ◽  
Zen Munawar
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Process Control ◽  
Soft Computing ◽  
Control Process ◽  
Good Method ◽  
Case Control ◽  
Added Value ◽  
Control Parameters ◽  
Soft Computing Techniques

[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.

scholarly journals Soft Computing Techniques for Process Control Applications

2011 ◽  
Vol 2 (3) ◽  
pp. 32-44 ◽  
Author(s):  
Rahul Malhotra ◽  
Narinder Singh ◽  
Yaduvir Singh
Keyword(s):  
Process Control ◽  
Soft Computing ◽  
Control Applications ◽  
Soft Computing Techniques

Soft case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 267-269 ◽  
Author(s):  
WILLIAM CHEETHAM ◽  
SIMON SHIU ◽  
ROSINA O. WEBER
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Network Theory ◽  
Probabilistic Reasoning ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Soft Computing Techniques ◽  
Neural Network Theory ◽  
Case Based

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

scholarly journals Applicability of Cellular Automata in Cryptanalysis

Cryptography ◽  
2020 ◽  
pp. 180-191
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Soft Computing ◽  
Search Problem ◽  
Extensive Literature ◽  
Soft Computing Techniques ◽  
A New Technique ◽  
The Given

Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.

Adaptive Neuro-Fuzzy Systems

2011 ◽  
pp. 31-36
Author(s):  
Larbi Esmahi ◽  
Kristian Williamson ◽  
Elarbi Badidi
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Tools And Techniques ◽  
Traditional Approaches

Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in fact easier to create as well (Jin, 2000). Thus was born the field of soft computing (Zadeh, 1994). Other techniques were added to this field, such as Artificial Neural Networks (ANN), and genetic algorithms, both modeled on biological systems. Soon it was realized that these tools could be combined, and by mixing them together, they could cover their respective weaknesses while at the same time generate something that is greater than its parts, or in short, creating synergy. Adaptive Neuro-fuzzy is perhaps the most prominent of these admixtures of soft computing technologies (Mitra et al., 2000). The technique was first created when artificial neural networks were modified to work with fuzzy logic, hence the Neuro-fuzzy name (Jang et al., 1997, pp. 1-7). This combination provides fuzzy systems with adaptability and the ability to learn. It was later shown that adaptive fuzzy systems could be created with other soft computing techniques, such as genetic algorithms (Yen et al., 1998, pp. 469-490), Rough sets (Pal et al., 2003; Jensen et al., 2004, Ang et al., 2005) and Bayesian networks (Muller et al., 1995), but the Neuro-fuzzy name was widely used, so it stayed. In this chapter we are using the most widely used terminology in the field. Neuro-fuzzy is a blanket description of a wide variety of tools and techniques used to combine any aspect of fuzzy logic with any aspect of artificial neural networks. For the most part, these combinations are just extensions of one technology or the other. For example, neural networks usually take binary inputs, but use weights that vary in value from 0 to 1. Adding fuzzy sets to ANN to convert a range of input values into values that can be used as weights is considered a Neuro-fuzzy solution. This chapter will pay particular interest to the sub-field where the fuzzy logic rules are modified by the adaptive aspect of the system. The next part of this chapter will be organized as follows: in section 1 we examine models and techniques used to combine fuzzy logic and neural networks together to create Neuro-fuzzy systems. Section 2 provides an overview of the main steps involved in the development of adaptive Neuro-fuzzy systems. Section 3 concludes this chapter with some recommendations and future developments.

Robotic Arm Movement Optimization Using Soft Computing

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Surender Kumar ◽  
Kavita Rani ◽  
V. K. Banga
Keyword(s):  
Optimal Control ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Target Position ◽  
Arm Movement ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Robot Arm ◽  
Soft Computing Techniques ◽  
Simulation Time

<p class="Text">Robots are commonly used in industries due to their versatility and efficiency. Most of them operating in that stage of the manufacturing process where the maximum of robot arm movement is utilized. Therefore, the robots arm movement optimization by using several techniques is a main focus for many researchers as well as manufacturer. The robot arm optimization is This paper proposes an approach to optimal control for movement and trajectory planning of a various degree of freedom in robot using soft computing techniques. Also evaluated and show comparative analysis of various degree of freedom in robotic arm to compensate the uncertainties like movement, friction and settling time in robotic arm movement. Before optimization, requires to understand the robot's arm movement i.e. its kinematics behavior. With the help of genetic algorithms and the model joints, the robotic arm movement is optimized. The results of robotic arm movement is optimal at all possible input values, reaches the target position within the simulation time.</p>

A Fuzzy Based Approach for Empirical Analysis of Unstructured Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4375-4379
Author(s):  
Mausumi Goswami ◽  
B. S. Purkayastha
Keyword(s):  
Fuzzy Logic ◽  
Empirical Analysis ◽  
Soft Computing ◽  
Engineering Problem ◽  
Data Uncertainty ◽  
Natural Languages ◽  
Text Data ◽  
Random Uncertainty ◽  
Soft Computing Techniques ◽  
Set Membership

Computational intelligence and soft computing has many promising technologies such as Text Mining. Document Classification using soft computing techniques like fuzzy logic helps to find a more practical solution due to ambiguity and uncertainty present in the text data. Uncertainty and information may be reflected as the part and parcel of any industrial or engineering problem to be solved. Information refers to the facts required to solve it and uncertainty refers to the non-random lack of certainty (‘non-random uncertainty’), ambiguity, haziness in the system. It is very important to ponder on the nature of uncertainty involved in a problem. Father of fuzzy logic, Lofti Zadeh (1965) suggested that decision-making using set membership is the key when it is required to deal with uncertainty. Fuzzy clustering helps to identify patterns which are difficult to be discovered using crisp clustering. Natural languages contain non-random uncertainty. To deal with non-random uncertainty or different degrees of truth or partial truth Fuzzy logic may be used. This work focuses on fuzzy logic based approaches being utilized for identification of coherent patterns. Empirical Analysis are conducted to realize and evaluate the effect of the methodology proposed.

Sign in / Sign up

Export Citation Format

Share Document