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Genetic Algorithms
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
In many fields, and especially in artificial intelligence, practitioners often combine various techniques in order to take advantage of the best characteristics of each technique. Most of these systems are known as hybrid systems. There are many examples of hybrid systems. Typically, you can find implementations that combine techniques. These implementations include: Using neural networks to adjust the parameters in a fuzzy expert system.Extracting symbolic information from a neural network to use in a rule-based system.Using genetic algorithms to create more efficient and compact neural networks. A popular approach is for a genetic algorithm to prepare a problem for genotype by a neural network. There are also implementations that use a neural network to support a genetic algorithm.Using genetic algorithms to tune the parameters of a fuzzy control system.Using case-based reasoning to supplement the problem-solving capabilities of an expert system.
Instrumentation and Controls
Published in Syed R. Qasim, Wastewater Treatment Plants, 2017
The next step in computer control systems is in the use of expert systems to control plant operations. This process, which is called fuzzy control system, expresses experts’ operation methods with IF-THEN control rules. The fuzzy reasoning is performed by inputting values of required parameters or by remote sensing of needed data from the wastewater treatment process. The program executes the data and performs feed-back control of the process, which again is monitored. Fuzzy control systems have been used to control the chemical needs for odor control. When working properly, fuzzy controls permit minute and smooth control of processes, reduce operator workloads, reduce chemical usage, reduce energy demand, and improve overall system performance.18
Modelling Non-Linear Rendering Processes
Published in Wong Gabriyel, Wang Jianliang, Real-Time Rendering: Computer Graphics with Control Engineering, 2017
The development of a fuzzy control system begins with the two key components: (1) the input–output membership functions describing the properties of the system (fuzzy sets) based on linguistic variables and (2) the rule-base that relates the input–output sets. Given an antecedent and consequent relationship between an input y to a SISO system’s output u using linguistic descriptions of their properties, the calculation may be represented as
A survey on control for Takagi-Sugeno fuzzy systems subject to engineering-oriented complexities
Published in Systems Science & Control Engineering, 2021
Yezheng Wang, Lei Zou, Lifeng Ma, Zhongyi Zhao, Jiyue Guo
Generally speaking, the fuzzy control systems include model-free and model-based ones. For model-free case, the fuzzy controller is deigned only based on human and prior knowledge. For model-based case, the original nonlinear plant is firstly represented by a suitable fuzzy system and then, the desired control laws can be constructed based on the obtained fuzzy model and the given performance index. During the last decades, both of these two types of fuzzy control systems/approaches have been investigated with extensive results and wide engineering applications (Cao et al., 1996; Jiang et al., 2020; Li et al., 2015, 2013; Li & Li, 2004; Qiu et al., 2010; Wang et al., 2018). The basic structure of a fuzzy control system consists of four conceptual components: knowledge base, fuzzification interface, inference engine, and defuzzification interface. A typical fuzzy system is usually represented by an ‘IF-THEN’ form. The ‘IF’ term refers to the antecedent (condition) part which is used to describe the situation concerning the system dynamics. The ‘THEN’ term refers to the consequent (conclusion) part which is used to describe the measures that should be taken. According to the literature (Sugeno, 1999), the fuzzy control systems can be classified into type-1, type-2, and type-3 ones based on the different ‘IF-THEN’ forms.
Application of Smart Dampers for Prevention of Seismic Pounding in Isolated Structures Subjected to Near-fault Earthquakes
Published in Journal of Earthquake Engineering, 2020
Arash Rayegani, Gholamreza Nouri
The membership function properties and the rules of the fuzzy control system have to be appropriately defined in order to have efficient performance. Setting the specifications of all parameters through a try and error method is an inaccurate and time-consuming procedure. For this reason, a genetic algorithm multi-objective optimization is used to optimize the fuzzy logic controller parameters. In this investigation, the rules are defined according to the practical view and will undergo no change in the process of optimization. Moreover, the parameters of membership functions are considered variable parameters in the optimization process, and the process is defined in such a way that the geometric properties of the input membership function evolve symmetrically. The defined rules for the output and two inputs of the control system are shown in Table 3 below.
Fuzzy logic-based energy management system of stand-alone renewable energy system for a remote area power system
Published in Australian Journal of Electrical and Electronics Engineering, 2019
Several control techniques are available in the literature for the implementation of energy management algorithm. Among them FLC has been found efficient due to lower power dissipation, optimised cost, reliability and stability (Merlin and Babu 2014). Fuzzy control system a control mechanism based on fuzzy set theory. As per the fuzzy theory and logic, a decision is made by mainly three operations: fuzzification process, an inference engine for rule base and defuzzification process (Zadeh 1965). It is a mathematical system that analyses analogue input values in terms of logical variables that take a continuous value between 0 and 1, unlike classical logic and operates on discrete values of either 1 or 0. It utilises the expert knowledge of an experienced user to design the knowledge base of the controller. Figure 6 represents the configuration of fuzzy logic (Lagorse, Simões, and Miraoui 2009; Männle 2000; Sala, Guerra, and Babuška 2005).