Titlebook: Nature-Inspired Design of Hybrid Intelligent Systems; Patricia Melin,Oscar Castillo,Janusz Kacprzyk Book 2017 Springer International Publi

我沒有辱罵

書目名稱Nature-Inspired Design of Hybrid Intelligent Systems影響因子(影響力)




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems影響因子(影響力)學(xué)科排名




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems網(wǎng)絡(luò)公開度




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems被引頻次




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems被引頻次學(xué)科排名




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems年度引用




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems年度引用學(xué)科排名




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems讀者反饋




書目名稱Nature-Inspired Design of Hybrid Intelligent Systems讀者反饋學(xué)科排名

山頂可休息

Brittle

https://doi.org/10.1007/978-3-319-47054-2Computational Intelligence; Nature-Inspired Optimization; Meta-Heuristics; Pattern Recognition; Time Ser

拒絕

白楊

Choquet Integral and Interval Type-2 Fuzzy Choquet Integral for Edge Detectionnterval type-2 Choquet integral is proposed. The aggregation operator is used as a method to integrate the four gradients of the edge detector. Simulation results with real images and synthetic images are presented and the results show that the interval type-2 Choquet integral is able to improve the detected edge.

homeostasis

A Gravitational Search Algorithm Using Type-2 Fuzzy Logic for Parameter Adaptationarameter and provide a different gravitation and acceleration to each agent in order to improve its performance. We test this approach with benchmark mathematical functions. Simulation results show the advantage of the proposed approach.

insular

Albinism

廢墟

A New Method Based on Modular Neural Network for Arterial Hypertension Diagnosiss of a modular neural network and its response with average integration. The proposed approach consists on applying these methods to find the best architecture of the modular neural network and the lowest prediction error. Simulations results show that the modular network produces a good diagnostic of the blood pressure of a patient.

Cognizance

A Competitive Modular Neural Network for Long-Term Time Series Forecastingthe modules are obtained from clustering the training data with a competitive layer. And each module consists of a single hidden layer nonlinear autoregressive neural network. This MNN architecture can be used for short-term and long-term time series forecasting.