International Journal of Computational Intelligence Systems

Volume 8, Issue sup1, December 2015

Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS-14)

1. Editorial

Irina Perfilieva
Pages: 1 - 2

2. Adaptive Input Selection and Evolving Neural Fuzzy Networks Modeling

Alisson Marques Silva, Walmir Caminhas, Andre Lemos, Fernando Gomide
Pages: 3 - 14
This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. models with larger and smaller number...

3. Forecasting Direction of Trend of a Group of Analogous Time Series Using F-Transform and Fuzzy Natural Logic

Vilém Novák, Irina Perfilieva
Pages: 15 - 28
We present an idea to group time series according to similarity of their local trends and to predict future direction of the trend of all of them on the basis of forecast of only one representative. First, we assign to each time series an adjoint one, which consists of a sequence of the F1-transform...

4. Bootstrapping DEA Scores for Road Safety Strategic Analysis in Brazil

Jorge Tiago Bastos, Yongjun Shen, Elke Hermans, Tom Brijs, Geert Wets, Antonio Clóvis Pinto Ferraz
Pages: 29 - 38
In this paper, three risk indicators on road safety are combined into a composite indicator in order to assess the overall fatality risk for the 27 Brazilian states using the so-called multiple layer data envelopment analysis model. The states are first clustered and next, a range of bootstrapped scores...

5. GA-Based Feature Selection Method for Imbalanced Data with Application in Radio Signal Recognition

Limin Du, Yang Xu, Jun Liu, Fangli Ma
Pages: 39 - 47
This paper presents an improved genetic algorithm (GA) based feature selection method for imbalanced data classification, which is then applied to radio signal recognition of ground-air communication. The proposed method improves the fitness function while SVM is selected as the classifier due to its...

6. A Method for Multi-attribute Decision Making Under Uncertainty Using Evidential Reasoning and Prospect Theory

Liuqian Jin, Xin Fang, Yang Xu
Pages: 48 - 62
In this paper, a method for multi-attribute decision making under uncertainty is proposed, the uncertainty is represented by certitude structure. In fact, there are both quantitative and qualitative attributes with different representation in multi-attribute decision making under uncertainty, so the...

7. Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

Jia Wu, Bi Wu, Shirui Pan, Haishuai Wang, Zhihua Cai
Pages: 63 - 74
Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes,...

8. -Resolution Method for Lattice-valued Horn Generalized Clauses in Lattice-valued Propositional Logic Systems

Weitao Xu, Wenqiang Zhang, Dexian Zhang, Yang Xu, Xiaodong Pan
Pages: 75 - 84
In this paper, an -resolution method for a set of lattice-valued Horn generalized clauses is established in lattice-valued propositional logic system ( ) based on lattice implication algebra. Firstly, the notions of lattice-valued Horn generalized clause, normal lattice-valued Horn generalized clause...

9. Semantics of Propositional Fuzzy Modal Logic with Evaluated Syntax and its Application to Fuzzy Decision Implications

Xiaodong Pan, Yang Xu
Pages: 85 - 93
This paper deals with propositional fuzzy modal logic with evaluated syntax based on MV-algebras. We focus on its semantical theory from the viewpoint of Pavelka's graded semantics of propositional fuzzy logic, investigate the L-tautologies based on different Kripke frames. We also define the notion...