COMPUTING

Soft Computing And Hard Computing For Large-Scale Plants: A Model

[Name of the Institution

Table of Content

CHAPTER 1: INTRODUCTION1

Background of research1

Problem Statement1

Aims and Objectives1

Purpose2

CHAPTER 2: LITERATURE REVIEW3

CHAPTER 3: METHODOLOGY5

Research Design5

Data Collection Method5

CHAPTER 04: DISCUSSION6

A general fusion model11

CHAPTER 05: CONCLUSION13

REFERENCES14

CHAPTER 1: INTRODUCTION

Background of research

The design of control systems for large-scale and complex industrial plants involves numerous trade-off problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness. Some of these parameters are even conflicting.

Problem Statement

The use of a multidiscipline approach is suggested to satisfy these requirements in an acceptable and well-balanced manner, and a fusion of soft computing and hard computing appears to be a natural and practical choice. Although the state-of-the-art soft computing technology has distinguished features, the use of soft computing technology would be ineffective, and may be doomed to fail, if it is improperly fused with conventional hard computing technology and control processes.

Aims and Objectives

The main aims and objectives of research is;

To explore general model of fusion is shown at the system level as well as at the algorithm level. In the system level, soft computing is applied to the upper level in a hierarchical control system, performing human-like tasks, such as forecasting and scheduling, or applied to ill-defined process models for carrying out intelligent control.

To describes Hard computing is used at the middle or lower control level for well-defined process models, carrying out coordinate control tasks while maintaining a high level of accurate and safety control.

Purpose

The purpose of this study is to explored the case of fusion at the algorithm level, this paper will discuss several types of tasks, such as scheduling and control.

CHAPTER 2: LITERATURE REVIEW

Soft computing is a collection of methodologies, including Fuzzy Systems, Neural Networks and Genetic Algorithms, designed to exploit tolerance for imprecision, uncertainty and partial truth in order to achieve tractability, robustness and low solution cost . It should be noted that evolutionary computation is a more general term, which includes genetic algorithms, evolution strategies, evolutionary programming and evolutionary algorithms . In this paper, such evolution-inspired methods are referred to as genetic algorithms. In engineering, imprecision, uncertainty and partial truth are mainly the result of complexity, nonlinearity, or variation of the control processes. Recent reviews on industrial applications of soft computing around the world and control technologies in Japan indicate that the number of successful soft computing-based products or engineering applications is increasing. This upward trend, however, does not mean that soft computing techniques are taking over the role of conventional hard computing. On the contrary, from a practicing engineer's point of view, many real-world problems can be solved competently by hard computing. Hard computing solutions are typically easier to analyze, their stability is highly predictable, and the computational burden of practical algorithms is usually low or moderate. Besides, the advantages and disadvantages of particular hard computing algorithms and methods are well-known and understood in research and development organizations. Rather than competitive, the emerging soft computing techniques and conventional hard computing techniques should be seen as complementary ...