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Author Archives: Yarpiz

Evolutionary ANFIS Training in MATLAB

Training of an ANFIS structure is a special kind of optimization problem. So metaheuristics and evolutionary algorithms can be used to train (tune the parameters of) an ANFIS structure. In this post, we are going to share with you, the MATLAB implementation of the evolutionary ANFIS training. The code, firstly creates an initial raw ANFIS structure and then uses Genetic ...

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Artificial Bee Colony in MATLAB

Artificial Bee Colony (ABC) is a metaheuristic algorithm, inspired by foraging behavior of honey bee swarm, and proposed by Derviş Karaboğa, in 2005. It is a simple, yet powerful algorithm, and can be used to solve wide variety of practical and real-world optimization problems. For more information on the Artificial Bee Colony algorithm you can refer to the related article ...

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Fuzzy PID Controller in MATLAB and Simulink

An approach to tune the PID controller using Fuzzy Logic, is to use fuzzy gain scheduling, which is proposed by Zhao, in 1993, in this paper. In this post, we are going to share with you, a MATLAB/Simulink implementation of Fuzzy PID Controller, which uses the blocksets of Fuzzy Logic Toolbox in Simulink. Three examples of the reference paper, are ...

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Feature Selection using Metaheuristics and EAs

Feature selection is one of common preprocessing tasks, which is performed to reduce the number of inputs of intelligent algorithms and models. This helps us to simplify the models, reduce the computation cost of model training, and enhance the generalization abilities of the model and prevention of over-training. For more information on feature selection concepts and methods, you can refer ...

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Nonlinear Regression using ANFIS

Adaptive Neuro-Fuzzy Inference System (ANFIS) is a combination of artificial neural network (ANN) and Takagi-Sugeno-type fuzzy system, and it is proposed by Jang, in 1993, in this paper. ANFIS inherits the benefits of both neural networks and fuzzy systems; so it is a powerful tool, for doing various supervised learning tasks, such as regression and classification. Fuzzy Logic Toolbox provides ...

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Bees Algorithm (BeA) in MATLAB

Bees Algorithm (BeA) is a metaheuristic optimization algorithm, inspired by food foraging behavior of honey bee colonies, and proposed by Pham et al., in 2005. In this algorithm, the mechanism of Waggle Dance is used to simulate the communication between bees. Better bees (solutions) have more opportunity to do waggle dance, and hence they are capable of attract more bees ...

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Group Method of Data Handling (GMDH) in MATLAB

Group Method of Data Handling (GMDH) is a family of mathematical modeling and nonlinear regression algorithms, which is originally proposed by Alexey Grigorevich Ivakhnenko, an Ukrainian scientist and mathematician, in 1968. This approach is also known as Polynomial Neural Network and can be assumed as a specific type of supervised Artificial Neural Network (ANN). In addition to modeling specifications, GMDH ...

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Firefly Algorithm (FA) in MATLAB

Firefly Algorithm (FA) is a metaheuristic algorithm for global optimization, which is inspired by flashing behavior of firefly insects. This algorithm is proposed by Xin-She Yang in 2008. Fireflies use the flashing behavior to attract other fireflies, usually for sending signals to opposite sex. However, in the mathematical model, used inside Firefly Algorithm, simply the fireflies are unisex, and any ...

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DBSCAN Clustering in MATLAB

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm, proposed by Martin Ester et al., 1996. The algorithm finds neighbors of data points, within a circle of radius ε, and adds them into same cluster. For any neighbor point, which its ε-neighborhood contains a predefined number of points, the cluster is expanded to contain its neighbors, ...

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Invasive Weed Optimization (IWO) in MATLAB

Invasive Weed Optimization (IWO) is a nature-inspired metaheuristic, inspired by spreading strategy of weeds, and proposed by Alireza Mehrabian and Caro Lucas, in 2006. Based on the r/K Selection Theory, the artificial weeds (solutions) use the r-Selection strategy in the beginning of algorithms, and gradually they switch to K-Selection strategy, as algorithm continues to running. For more information on the ...

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