José Soria

Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

A new approach for time series prediction using ensembles of ANFIS models

This paper describes an architecture for ensembles of ANFIS (adaptive network based fuzzy inference system), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that we are considered are: the Mackey-Glass, Dow Jones and Mexican stock exchange. The methods used for the integration of the ensembles of ANFIS are: integrator by average and the integrator by weighted average. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.Performance of different ACO variants was observed to choose one as the basis to the proposed approach.Convergence fuzzy controller with the objective of maintaining diversity to avoid premature convergence was created. Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.

Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system

Highlights? Hybrid intelligent system for arrhythmia classification. ? Combination of fuzzy KNN with neural networks with Mamdani fuzzy system. ? ECG signal transformation for improving classification results. In this paper we describe a hybrid intelligent system for classification of cardiac arrhythmias. The hybrid approach was tested with the ECG records of the MIT-BIH Arrhythmia Database. The samples considered for classification contained arrhythmias of the following types: LBBB, RBBB, PVC and Fusion Paced and Normal, as well as the normal heartbeats. The signals of the arrhythmias were segmented and transformed for improving the classification results. Three methods of classification were used: Fuzzy K-Nearest Neighbors, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, a Mamdani type fuzzy inference system was used to combine the outputs of the individual classifiers, and a very high classification rate of 98% was achieved.

Dynamic Fuzzy Logic Parameter Tuning for ACO and Its Application in TSP Problems

Ant Colony Optimization (ACO) is a population-based constructive metaheuristic that exploits a form of past performance memory inspired by the foraging behavior of real ants. The behavior of the ACO algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired algorithms. The present paper explores a new approach of diversity control in ACO. The central idea is to avoid or slow down full convergence through the dynamic variation of the alpha parameter. The performance of different variants of the ACO algorithm was observed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence was created. Encouraging results on several travelling salesman problem (TSP) instances are presented with the proposed method.

A generalized type-2 fuzzy granular approach with applications to aerospace

In this paper a granular approach for intelligent control using generalized type-2 fuzzy logic is presented. Granularity is used to divide the design of the global controller into several individual simpler controllers. The theory of alpha planes is used to implement the generalized type-2 fuzzy systems. The proposed method for control is applied to a non-linear control problem to test the advantages of the proposed approach. Also an optimization method is used to efficiently design the generalized type-2 fuzzy system to improve the control performance.

New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system

In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy. The simulation results show that the proposed algorithm achieves better results than the classical ACO algorithm for the design of the fuzzy controller.

A Method to Solve the Traveling Salesman Problem Using Ant Colony Optimization Variants with Ant Set Partitioning

In this paper we propose an ant’s partition method for Ant Colony Optimization (ACO), a meta-heuristic that is inspired in ant’s behavior and how they collect their food. The proposed method equivalently divides the total number of ants in three different subsets and each one is evaluated separately by the corresponding variation of ACO (AS, EAS, MMAS) to solve different instances of The Traveling Salesman Problem (TSP). This method is based on the idea of “divide and conquer” to be applied in the division of the work, as the ants are evaluated in different ways in the same iteration. This method also includes a stagnation mechanism that stops at a certain variation if it’s not working properly after several iterations. This allows us to save time performing tests and have less overhead in comparison with the conventional method, which uses just one variation of ACO in all iterations.

Reactive control of a mobile robot in a distributed environment using fuzzy logic

This paper describes reactive control of a mobile robot using fuzzy logic in a distributed environment. Simulation results of the reactive fuzzy controller in a particular maze problem illustrate the effectiveness of the proposed approach. The mobile robot is able to solve the maze problem with the use of fuzzy rules designed with expert knowledge.

Reactive and tracking control of a mobile robot in a distributed environment using fuzzy logic

This paper describes reactive and tracking control of a mobile robot using integration methods to combine the response of both types of controls based on fuzzy logic. Simulation and experimental results of the complete system demonstrates the effectiveness of the proposed approach.