The Use of Soft Computing in Management

2014 ◽  
pp. 294-326 ◽  
Author(s):  
Petr Dostál
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Computing Methods ◽  
Social Psychological ◽  
Decision Making Processes ◽  
Soft Computing Methods

The decision-making processes in management are very complicated because they include political, social, psychological, economic, financial, and other factors. Many variables are difficult to measure; they may be characterized by imprecision, uncertainty, vagueness, semi-truth, approximations, and so forth. Soft computing methods have had successful applications in management. Nowadays the new theories of soft computing are used for these purposes. The applications in management have specific features in comparison with others. The processes are focused on private corporate attempts at money making or decreasing expenses. The soft computing methods help in decentralization of decision-making processes to be standardized, reproduced, and documented. There are various soft computing methods used in management-classical ones and methods using soft computing. Among soft computing methods there belongs fuzzy logic, neural networks, and evolutionary algorithms. The use of the theories mentioned previous is important also in the sphere of analysis and simulation. The case studies are discussed in the article. It can be mentioned, for example, which way should be used to address the potential customer (fuzzy logic), which kind of customer could be provided by a loan or a mortgage (neural networks), the sorting of products according to the kind of customers (genetic algorithms), or solving the travelling salesman problem (evolutionary algorithms).

The Use of Soft Computing in Management

2016 ◽  
pp. 1541-1579
Author(s):  
Petr Dostál
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Computing Methods ◽  
Social Psychological ◽  
Decision Making Processes ◽  
Soft Computing Methods

The decision-making processes in management are very complicated because they include political, social, psychological, economic, financial, and other factors. Many variables are difficult to measure; they may be characterized by imprecision, uncertainty, vagueness, semi-truth, approximations, and so forth. Soft computing methods have had successful applications in management. Nowadays the new theories of soft computing are used for these purposes. The applications in management have specific features in comparison with others. The processes are focused on private corporate attempts at money making or decreasing expenses. The soft computing methods help in decentralization of decision-making processes to be standardized, reproduced, and documented. There are various soft computing methods used in management-classical ones and methods using soft computing. Among soft computing methods there belongs fuzzy logic, neural networks, and evolutionary algorithms. The use of the theories mentioned previous is important also in the sphere of analysis and simulation. The case studies are discussed in the article. It can be mentioned, for example, which way should be used to address the potential customer (fuzzy logic), which kind of customer could be provided by a loan or a mortgage (neural networks), the sorting of products according to the kind of customers (genetic algorithms), or solving the travelling salesman problem (evolutionary algorithms).

scholarly journals A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

2021 ◽  
Vol 10 (4) ◽  
pp. 37-42
Author(s):  
M.P.L. Perera
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Artificial Intelligent ◽  
Human Decision ◽  
Decision Making Processes ◽  
E Learning ◽  
Intelligent Approach ◽  
Learning Frameworks

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

SOFT COMPUTING: AN INTRODUCTION

2018 ◽  
Vol 8 (6) ◽  
pp. 63
Author(s):  
Matthew N. O. Sadiku ◽  
Yonghui Wang ◽  
Suxia Cui ◽  
Sarhan M. Musa
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Low Cost ◽  
Computational Techniques ◽  
Inaccurate Information ◽  
Major Benefit ◽  
Partial Truth ◽  
Human Decision

Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

scholarly journals Studies of Manufacturing Process Control: A Technological Transfer of Soft Computing

2008 ◽  
pp. 31-37
Author(s):  
J. P. Panda ◽  
R. N. Satpathy
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Technology Transfer ◽  
Evolutionary Algorithms ◽  
Soft Computing ◽  
Manufacturing Processes ◽  
Technological Transfer ◽  
Manufacturing Process Control

The field of soft computing embraces several techniques that have been inspired by nature but are mathematical. These techniques are artificial neural networks, fuzzy logic and evolutionary algorithms. Often these techniques are considered part of artificial intelligence, however the name artificial intelligence is more properly given to techniques which try to capture and emulate biological intelligence, such as expert systems and thinking computers. This paper focuses on the technology transfer issues and solutions when using soft computing for off line control of manufacturing processes. This paper will discuss each of these three techniques – neural networks, fuzzy logic and evolutionary algorithms - in turn and how they might be used in manufacturing. The kind of problems these techniques are best suited for will be defined, and competing techniques will be compared and contrasted.

A Soft Computing Overview

2010 ◽  
pp. 1-11
Author(s):  
Marcos Gestal ◽  
Daniel Rivero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Computing Methods ◽  
Testing System ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Computation Algorithms ◽  
Brain Behavior ◽  
The Brain

Nature has proved to be the best testing system, where we can analyze the effectiveness of any method of solving problems. It provides one of the most complex problems to be resolved: the survival. Analyzing how the species behave to achieve that survival, soft computing methods try to mimic this behavior to provide meaningful solutions to diverse problems. This chapter offers an introduction the fundamentals that the different soft computing techniques translate from Nature. It includes an approach of the brain behavior (Artificial Neural Networks) or the evolution ideas taken from Darwin’ laws (Evolutionary Computation algorithms).

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2012 ◽  
pp. 444-466
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

scholarly journals A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

2021 ◽  
Vol 10 (4) ◽  
pp. 37-42
Author(s):  
M.P.L. Perera*
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Artificial Intelligent ◽  
Human Decision ◽  
Decision Making Processes ◽  
E Learning ◽  
Intelligent Approach ◽  
Learning Frameworks

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2011 ◽  
pp. 372-394
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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