Easy to use Fuzzy of Matlab

Feb 13, 20086 likes1,298 views

Lewis Chen

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The document describes how to use MATLAB's Fuzzy Logic Toolbox to solve fuzzy logic problems. It begins with an introduction to fuzzy logic and an overview of the toolbox. It then uses the example of balancing an inverted pendulum on a cart to demonstrate the fuzzy inference system design process. This involves defining membership functions, rules, and using toolbox tools to simulate the fuzzy controller.

Fuzzy and nnShimi Haridasan

The document discusses fuzzy logic and artificial neural networks. It provides an overview of fuzzy logic, including fuzzy sets, membership functions, fuzzy linguistic variables, fuzzy rules and fuzzy control. It also covers artificial neural networks, including the biological inspiration from the human brain, basic neuron models, multi-layer feedforward networks, training algorithms like gradient descent, and examples of neural networks solving problems like XOR classification. Hardware implementations on systems like DSpace and Opal RT are also briefly mentioned.

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This document provides an introduction to a MATLAB supplement for the book "Fuzzy and Neural Approaches in Engineering". It describes MATLAB as an educational software package for technical computing. The supplement contains MATLAB code examples that demonstrate concepts from the book, such as neural networks, fuzzy logic, and hybrid systems. It is intended to help readers gain a practical understanding of implementing soft computing techniques in MATLAB.

Fuzzy Logic and Neural NetworkSHIMI S L

Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than fixed and exact. It was introduced in 1965 with the proposal of fuzzy set theory by Lotfi Zadeh. Fuzzy logic uses fuzzy sets and membership functions to deal with imprecise or uncertain inputs and allows for reasoning that allows for partial truth of inputs between fully true and fully false. Fuzzy controllers combine fuzzy logic with control theory to control complex systems. They involve fuzzification of inputs, applying fuzzy rules through inference, and defuzzification of outputs to obtain a crisp control action.

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This document describes a study conducted by undergraduate students at Uva Wellassa University of Sri Lanka on applying fuzzy logic to aircraft landing control. It provides background on fuzzy logic and fuzzy set theory. It then presents the students' simulation of an aircraft's final descent and landing approach, where fuzzy logic is used to control the aircraft's vertical velocity based on its current height above ground. Over multiple cycles, the simulation demonstrates how the fuzzy logic system gradually reduces the aircraft's velocity as it gets closer to landing for a soft touchdown.

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Application of fuzzy logicViraj Patel

Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.

Genatic AlgorithmYasir Khan

The document discusses genetic algorithms, which are search and optimization techniques inspired by biological evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions to problems iteratively. They have been successfully applied to problems like the traveling salesman problem. The document covers the basic components of a genetic algorithm, including encoding solutions, initializing a population, evaluating fitness, selecting parents, and modifying offspring through genetic operators. It also discusses implementation considerations and examples of genetic algorithm applications.

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This document is a user's guide for version 3.0 of the Neural Network Toolbox. It introduces neural networks and their applications. Key features of version 3.0 include a reduced memory Levenberg-Marquardt training algorithm, new network types like probabilistic neural networks and generalized regression networks, modular network representations, improved Simulink support, and general toolbox improvements. The guide provides basic information on neural network concepts and architectures.

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