Titlebook: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization; Patricia Melin,Oscar Castillo,Janusz

诱骗

https://doi.org/10.1007/978-3-030-44286-6g rate or number of epochs are static values and are not optimized. Here, the MIT-BIH arrhythmia database with 15 classes was used. Results show that using 5 modules architecture could be a good approach for classification of arrhythmias.

Infinitesimal

GRUEL

https://doi.org/10.1007/978-3-030-10991-2 Mackey Glass benchmark time series. Simulation results are presented for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy response integration and its optimization with genetic algorithms. The Simulation results show that the ensemble approach produces good prediction of the Mackey Glass time series.

椭圆

Thomas Buehrmann,Ezequiel Di Paolod Bell and Triangular membership functions for each of the fuzzy integrators; in the interval type-2 fuzzy integrator there are more parameters, thereby increasing the complexity of the training for the fuzzy integrators. Simulation results show the effectiveness of the proposed approach in comparison with other researchers.

不近人情

薄荷醇

Color Image Edge Detection Method Based on Interval Type-2 Fuzzy Systemse detector than a grayscale edge detector. The proposed method is applied over a database of color images that include synthetic and real images. The performance of the proposed method is compared with other edge detection algorithms such as Sobel combined with type-1 fuzzy systems T1FSs and the traditional Sobel operator.

陈腐的人

Method for Measurement of Uncertainty Applied to the Formation of Interval Type-2 Fuzzy Setsn are modified according to the found index values, obtaining all antecedents in the process. Afterwards, the Cuckoo Search algorithm is used to optimize the Interval Sugeno consequents of the Fuzzy Inference System. Some sample datasets are used to measure the output interval coverage.

APRON

Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning for the Dow-Jones Time Seri. The proposed method is based on recent methods that handle weight adaptation and especially fuzzy weights. In this work neural networks with type-1 fuzzy weights or type-2 fuzzy weights are presented. The proposed approach is applied to the case of Dow-Jones time series prediction for evaluating its efficiency.

子女

Gingivitis