Fuzzy Logic Based MATLAB Projects

Fuzzy Signal Detection in Multiple-access Ultra Wide Band Communication Systems

Ultra-wide band (UWB) communications transmits a wide bandwidth signal with an extremely low power spectral density. This property of UWB makes it possible to co-exist with the current narrowband communication systems operating at dedicated frequency bands.

Ultra-wide band can also serve multiple users by using the Spread Spectrum (SS) technique. However, with the number of multiple users increasing, signals associated with users will interfere with each other, resulting in Multi-Access Interference (MAI), a drawback in MA-UWB systems, which could adversely affect the system performance.

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An Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller for Food Classification

A portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programm the SFA in a microcontroller.

The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analyzed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

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A Dynamic Fuzzy Controller to Meet Thermal Comfort by using Neural Network Forecasted Parameters as the Input

Heating, ventilating and air-conditioning (HVAC) systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed.

Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject.

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Fuzzy Controlled SVC for Transmission Line

The primary purpose of this project is to create and simulate the logic that is fuzzy of firing angle for SVC to be able to achieve better, smooth and adaptive control of reactive power. The design, modeling and simulations are carried away for λ /8 Transmission line and the compensation is positioned at the end that is receiving load end.

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Designing an Optimal Fuzzy Logic Controller of a DC Motor

The use of Particle Swarm Optimization for designing an optimal fuzzy logic controller of a DC Motor is presented in this project. The approach used in this project is to optimize the membership functions of a logic that is fuzzy using Particle Swarm Optimization. Further, the answers are determined using Simulink.

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Neuro-Fuzzy Wavelet based Adaptive Mppt Algorithm for Photovoltaic Systems Using MATLAB

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

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Evidence based Uncertainty Models and Particles Swarm Optimization for Multiobjective Optimization of Engineering Systems Using MATLAB

The present work develops several methodologies for solving engineering analysis and design problems involving uncertainties and evidences from multiple sources. The influence of uncertainties on the safety/failure of the system and on the warranty costs (to the manufacturer) are also investigated. Both single and multiple objective optimization problems are considered. A methodology is developed to combine the evidences available from single or multiple sources in the presence (or absence) of credibility information of the sources using modified Dempster Shafer Theory (DST) and Fuzzy Theory in the design of uncertain engineering systems. To optimally design a system, multiple objectives, such as to maximize the belief for the overall safety of the system, minimize the deflection, maximize the natural frequency and minimize the weight of an engineering structure under both deterministic and uncertain parameters, and subjected to multiple constraints are considered.

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Genetic Algorithm: An Authentic tool for Agriculture Business System implemented by MATLAB

Genetic Algorithms are probabilistic algorithms that belong to the larger class of Evolutionary Algorithms, a family of computational models inspired by the process of natural evolution.As in the evolutionary process of species, GAs manipulate a population of individuals, each of them with an associated fitness value, to a new generation of individuals, using the Darwinian principles of reproduction and survival of the fittest. Each individual of the population represents a possible solution; therefore, the GA searches among the set of solutions in the search space, always toward the global optimum, the individual with the highest aptitude. The standard procedure of the GAs in its simplest configuration is presented in Fig. 1. The most important steps of the algorithm are the generation of the initial population, the evaluation of the fitness function and the generation of a new population of individuals through the application of the genetic operators: selection, recombination and mutation.

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Power Load Balancing Using Fuzzy Logic Using MATLAB

Phase balancing generally occurs during minimization of loss, planning and restoration of energy in distribution systems. Some areas may be over loaded due to fault in distribution. In order to overcome these problems, power controlling and there by controlling of load is required for those areas. It leads to load balancing technique. In these papers, load balancing is achieved by fuzzy logic control. Input to the fuzzy is the total load of the feeders. The output of the fuzzy step is the input to the load balancing system. Load balancing system utilizes optimization technique to convert kilowatts values into load points and specify the load points.

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Simulation of Riding a Bicycle Using Simulink Using MATLAB

Training to ride a bicycle in a race requires a rider to maintain different cadences for the give situation. One situation where most riders try to maintain a cadence is hill climbing. In order to train for hill climbing a rider needs to have hills to climb. If the rider lives in an area without hills then training for hills becomes more difficult. To help a rider train for hill climbing in areas without hills a device that is built into a bike to simulate hills is proposed. To aid in the design of such a device a simulation of a bicycle was built in Simulink. A fuzzy logic controller was designed to control the cadence through the manipulation of the applied force. Using another fuzzy logic controller a gear shifting was attempted. The simulation data about how a bicycle performs was generated with good accuracy. The cadence controller was able to control the cadence with little to no overshoot and a small about of steady state error. The gear shifting system caused Simulink to fail due to the singularity created when the gear changed. Overall the bicycle simulation could be used to develop a hill simulation device. The addition of a gear selection system would be beneficial to the testing of a hill simulation device because it would allow the device to be tested during gear changes.

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Smart Farm: Automated Classifying and Grading System of Tomatoes using Fuzzy Logic Using MATLAB

Manual operation is considered as a big factor in a low production and the Smart Farm System is one way that can address this problem by improving and increasing the quality and quantity of production by making farms more intelligent and more connected through the precision agriculture. With that, the proponents will develop a system through smart farm system that is capable of classifying and grading the tomatoes. This process will be done automatically using image processing and fuzzy logic. There will be a Fuzzy Inference Systems sto be established using MATLAB software to classify and grade the tomato fruit. In classifying, system will determine if tomato is damaged or not. On the other hand, system will distinguish if a specific fruit or crop is under ripe, ripe or overripe in grading. It is believed that this study is of great help to farmers for high yield and productive plant harvests.

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