Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

Numeracy Predicts More Effortful and Elaborative Search Strategies in a Complex Risky Choice Context: A Process‐Tracing Approach

The goal of the current study was to explore information search and processing differences between individuals who are less and more numerate in an attempt to better understand the mechanisms that might differentiate the choices they make. We did so using a computerized process‐tracing system known as MouseTrace, which presented monetary gambles in an alternative × attribute matrix with outcome (dollar amount) and probability information as attributes. This information was initially occluded but could be revealed by clicking on the cell that contained the desired information. Beginning with nine gambles offering the chance of gaining or losing a specified amount, participants (N = 110) narrowed down the options (to three and then one) using an inclusion or exclusion strategy. Consistent with previous research, inclusion was a more effortful strategy, and individuals who were higher in numeracy were more likely to select prospects with the highest expected value. Process measures revealed these individuals expended more effort (i.e., attended to and sought out more information and processed it in greater depth) and exhibited more compensatory processing than those who were lower in numeracy, but this sometimes depended on whether one was asked to include or exclude. These results serve as further evidence that individuals with higher levels of numeracy often engage in more elaborative processing of the decision task, which tends to lead to more optimal choices. However, they also suggest that individuals are adaptive and that the specific situation can matter. Copyright © 2016 John Wiley & Sons, Ltd.     

Characterizing Simpler Recognizable Sets of Integers

For a given numeration system U, a set X of integers is said to be U-star-free if the language of the normalized U-representations of the elements in X is star-free. Adapting a result of McNaughton and Papert, we give a first-order logical characterization of these sets for various numeration systems including integer base systems and the Fibonacci system. For k-ary systems, the problem of the base dependence of this property is also studied. Finally, the case of k-adic systems is developed.     

Resource-origins of Nonmonotonicity

Formal nonmonotonic systems try to model the phenomenon that common sense reasoners are able to “jump” in their reasoning from assumptions Δ to conclusions C without their being any deductive chain from Δ to C. Such jumps are done by various mechanisms which are strongly dependent on context and knowledge of how the actual world functions. Our aim is to motivate these jump rules as inference rules designed to optimise survival in an environment with scant resources of effort and time. We begin with a general discussion and quickly move to Section 3 where we introduce five resource principles. We show that these principles lead to some well known nonmonotonic systems such as Nute’s defeasible logic. We also give several examples of practical reasoning situations to illustrate our principles. Edited by Hannes Leitgeb     

条件推理中的二元效应初探

通过预备实验选取前件(或后件)是二元(或多元)的5种条件命题的4种推理形式作为实验材料,以大学本科生为被试,考察了五种不同条件命题类型对4种推理结果的影响.结果表明命题类型、推理规则以及两者的交互作用都非常显著.这个结果进一步验证了条件推理的集合映射模型.     

Your divided attention, please! The maintenance of multiple attentional control sets over distinct regions in space

When non-informative peripheral cues precede a target defined by a specific feature, cues that share the critical feature will capture attention while cues that do not will be effectively ignored. We tested whether different attentional control sets can be simultaneously maintained over distinct regions of space. Participants were instructed to respond only to specific colored targets at specific locations. Most trials included non-predictive cues whose location and/or color were either congruent or incongruent with the spatial and/or color properties of the impending target. We observed contingent capture only for cues that were consistent with the attentional control set applied to that region of space. This is the first demonstration that separate attentional control sets can be simultaneously maintained at distinct spatial locations, with implications for the flexibility of endogenous control over automatic attentional orienting.     

MV-Algebras and Quantum Computation

We introduce a generalization of MV algebras motivated by the investigations into the structure of quantum logical gates. After laying down the foundations of the structure theory for such quasi-MV algebras, we show that every quasi-MV algebra is embeddable into the direct product of an MV algebra and a “flat” quasi-MV algebra, and prove a completeness result w.r.t. a standard quasi-MV algebra over the complex numbers. Presented by Heinrich Wansing     

Component Models for Fuzzy Data

The fuzzy perspective in statistical analysis is first illustrated with reference to the “Informational Paradigm” allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to J LR fuzzy variables as observed on I observation units. Each observation is characterized by its center and its left and right spreads (LR₁ fuzzy number) or by its left and right “centers” and its left and right spreads (LR₂ fuzzy number). Two types of component models for LR₁ and LR₂ fuzzy data are proposed. The estimation of the parameters of these models is based on a Least Squares approach, exploiting an appropriately introduced distance measure for fuzzy data. A simulation study is carried out in order to assess the efficacy of the suggested models as compared with traditional Principal Component Analysis on the centers and with existing methods for fuzzy and interval valued data. An application to real fuzzy data is finally performed. We would like to express our gratitude to the Editor, the Associate Editor, and the Referees whose comments and suggestions improved significantly the quality of the paper.