Denisse Hidalgo

An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms

This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization method, we propose the use of a genetic algorithm (GA) to optimize the type-2 fuzzy inference systems, considering different cases for changing the level of uncertainty of the membership functions to reach the optimal solution at the end.

Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms

We describe in this paper a comparative study between fuzzy inference systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms with the goal of having optimized versions of both types of fuzzy systems. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy systems of integration. The comparative study of the type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.

Optimization with genetic algorithms of modular neural networks using interval type-2 fuzzy logic for response integration: The case of multimodal biometry

We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and gaussian; since these algorithms can generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods of modular neural networks for multimodal biometry.

Optimization of Type-2 Fuzzy Integration in Modular Neural Networks Using an Evolutionary Method with Applications in Multimodal Biometry

We describe in this paper a new evolutionary method for the optimization of a modular neural network for multimodal biometry The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions and rules) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using type-1 and type-2 fuzzy inference systems.

Optimal Design of Type-2 Fuzzy Membership Functions Using Genetic Algorithms in a Partitioned Search Space

In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.

Interval type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimisation with genetic algorithms

In this paper a comparative study of fuzzy inference systems as methods of integration in Modular Neural Networks (MNNs) for multimodal biometry is presented. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimised with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate the fuzzy systems automatically. Then the response integration of the MNN was tested with the optimised fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behaviour of the two different integration methods of MNNs for multimodal biometry.

Optimization of Modular Neural Networks with Interval Type-2 Fuzzy Logic Integration Using an Evolutionary Method with Application to Multimodal Biometry

In this paper we describe a new evolutionary method to perform the optimization of a modular neural network applied to the case of multimodal biometry. Integration of responses in the modular neural network is performed using type-1 and type-2 fuzzy inference systems.

Optimization method for membership functions of type-2 fuzzy systems based on the level of uncertainty applied to the response integration of modular neural network for multimodal biometrics

This research proposes a method for optimizing the membership functions of type-2 fuzzy systems based on their level of uncertainty. The proposed new method of optimization considers three different cases of uncertainty (Footprint of Uncertainty) and obtains an optimal type-2 fuzzy system. Such cases have been called Case 1, Case 2 and Case 3. The first case is distinguished by having the same footprint of uncertainty for all existing membership functions of the fuzzy system inputs. The second case has a different footprint of uncertainty for the different inputs. And finally, the third case, which has a different footprint of uncertainty for each membership function of each input. The experimental results were tested in modular neural networks for multimodal biometry.

Evolutionary optimization of type-2 fuzzy systems based on the level of uncertainty

In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.

Type-2 fuzzy inference system optimization based on the uncertainty of membership functions applied to benchmark problems

In this paper we describe a method for the optimization of type-2 fuzzy systems based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space. The proposed method produces the best fuzzy inference systems for particular applications based on a genetic algorithm. We apply a Genetic Algorithm to find the optimal type-2 fuzzy system dividing the search space in three subspaces. We show the comparative results obtained for the benchmark problems.