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Themain aim of this paper is to use fuzzy inference systems for controlling the relevant parameters within the equations of the FWA algorithm. In other words, parameters that are considered constant in the traditional FWA and are now made dynamic by using fuzzy logic. It is worth mentioning that we also made a small modification to the algorithm with the goal of having a better performance and the...
The main goal of the work presented in the paper is to introduce the use of fuzzy logic in the Grey Wolf Optimizer (GWO) algorithm specifically for dynamic simultaneous adaptation of the key parameters, which are crucial in the performance of the metaheuristic. The proposed approach for this modification of GWO using fuzzy logic is presented. In addition, a brief comparison between the traditional...
Research in echocardiogram imaging it's very important because it allows assessing both anatomy and cardiac function, help diagnose various diseases. In this paper to find the optimal architecture of a MNN, where means finding the number of layers and nodes. In this case the Type-2 fuzzy logic gravitational search algorithm is used for optimizing the MNN for pattern recognition in echocardiogram imaging.
In this work a new metaheuristic of optimization bio-inspired on the plants self-defense techniques applied to optimization problems is presented. Plants are living beings that are part of a habitat, and in some recent works the authors claim that plants are able to react to different external stimuli. In nature, plants are exposed to a variety of predatory animals such as bacteria, fungi, insect...
In this paper, a comparison of the Choquet and Sugeno integrals is presented. The proposed methods enable the calculation of the Choquet and Sugeno integrals to combine multiple source of information with a degree of uncertainty. The methods are used to combine the modules output of a modular neural network for face recognition and a comparison is performed. In this paper, the focus is on aggregation...
Edge detection is an essential step used in image processing systems and can be applied to image sets before the training phase in pattern recognition systems to improve performance. An edge detector simplifies the analysis of the images; because, it reduces the data to be processed by highlighting the most important features. In this paper we show the advantage of using a fuzzy edge detector method...
In this paper introduces the optimization of ensemble neural networks with fuzzy integration type-1 and type-2 for application of the prediction of complex time series, the methods used for optimization are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimization of ensemble neural networks for integration of network response is made with typo-1 and type-2 fuzzy systems. The...
According to the literature of Particle Swarm Optimization (PSO), there are problems of getting stuck at local minima and premature convergence with this algorithm. A new algorithm is presented in this paper called the Improved Particle Swarm Optimization using the gradient descent method as an operator incorporated into the Algorithm, as a function to achieve the improvement. The gradient descent...
Edge detection is an essential method used in the image processing systems and can be applied to image sets before the training phase in pattern recognition systems. An edge detector simplifies the analysis of the images; because, it reduces the dataset processed. In this paper we present the advantage to use a fuzzy edge detector method in a face recognition system. In the methodology, first the...
In this paper, a comparison of the Choquet and Sugeno integrals is presented. The proposed methods enable the calculation of the Choquet and Sugeno integrals for combining multiple source of information with a degree of uncertainty. The methods are used to combine the modules output of a modular neural network for face recognition. In this paper, the focus is on aggregation operators that use measures...
We describe in this paper the architecture of a modular neural network (MNN) for pattern recognition. More recently, the study of modular neural network techniques theory has been receiving significant attention. The design of a recognition system also requires careful attention. The paper aims to use the Ant Colony paradigm to optimize the architecture of this Modular Neural Network for pattern recognition...
In this paper a Modular Neural Network (MNN) with a granular approach optimization is proposed, where a firefly optimization is proposed to design a optimal MNN architecture. The proposed method can perform the optimization of some parameters such as; number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each...
In this paper two bio-inspired methods are used to optimize the type-2 fuzzy inference system integrator in an ensemble of three neural networks with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the type-2 fuzzy system integrators that work in response integration of the ensemble neural network for obtaining the final output. In this work an optimized...
We propose in this paper the use fuzzy logic to adjust parameters in the fireworks algorithm (FWA), that is, parameters that usually are considered as constants in the algorithm, we have transformed them to be dynamic parameters in the FWA. First, we realized an exhaustive experimentation of the parameters of the FWA algorithm, with the purpose of selecting the parameters that have more effect on...
In this paper, a new classification method based on LVQ neural networks and Fuzzy Logic is presented. This new fuzzy LVQ method (FuzzLVQ) mainly focuses on distances between the input vector and the cluster centers, randomly generated, thus the fuzzy system in the FuzzLVQ method is used to determine the shortest distance, and with this information, the cluster center can be approached to input vector...
In this paper a new method for response integration, based on the Choquet Integral with Interval type-2 Sugeno measures is presented. The Choquet integral is used as a method to integrate the outputs of the modules of the modular neural networks (MNN). The fuzzy Sugeno measures of the Choquet integral are represented by an interval type-2 fuzzy system. A database of faces was used to perform the preprocessing,...
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Dow Jones time series. The Dow Jones time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble...
There are several factors that can influence the selection of electives in order to complete the set of credits of a course in Bachelor level. Even though this problem has been studied repeatedly by many researchers on literature, the results have not established optimal values using bio-inspired algorithms to analyze the cost-benefit for every student in a minority group, and comparing their choices...
This paper describes the design with Particle Swarm Optimization of a neural network ensemble with type-1 and type-2 fuzzy integration of responses. The proposed ensemble neural network approach is tested with the problem of time series prediction. The time series that is being considered for testing the hybrid approach is the US/Dollar MX time series. Simulation results show that the ensemble neural...
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neurofuzzy inferences systems) models for the prediction of the Mackey-Glass time series. The considered a chaotic system is the Mackey-Glass time series that is generated from the differential equations, so this benchmark time series is used to the test of performance of the proposed ensemble...
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