Modeling fuzzimetric cognition of technical analysis decisions: reducing emotional trading

Stock traders' forecasting strategies are mainly dependent on Technical Analysis (TA) indicators. However, some traders would follow their intuition and emotional aspects when trading instead of following the mathematically solid forecasting techniques of TA(s). The objective of this paper is to help traders to rationalize their choices by generating the maximum and minimum tolerances of possible prices (termed in this paper as "fuzzy spectrum") and hence reducing their "emotional" trading decisions. This would be an important aspect towards avoiding an undesired outcome. Fuzzy logic has been used in this paper to identify such tolerances based on the most popular TA(s). Fuzzification of these TA(s) was used via a modular approach of fuzzy logic and by adopting "fuzzimetric sets" described in this paper to achieve the "fuzzy spectrum" of forecasted price tolerances when buying and selling decisions. Experimental results show the success of developing the "fuzzy spectrum" based on the "fuzzy" tolerances discovered from the TA(s) outputs. As a result, this paper contributes towards a better "rationalized" decision making when it comes to buying and selling stocks in this kind of industry

Extracting takagi-sugeno fuzzy rules with interpretable submodels via regularization of linguistic modifiers

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases. © 2006 IEEE

Fuzzy logic integration into construction management planning

ENGLISH: A construction of an asset is challenging work associated with huge responsibilities. Project manager’s main challenges in construction are generally Time-Cost-Quality dependent. In addition to these factors, Project Managers or construction Manager have to deal with numerous uncertainties in the process of construction. Considering its diverse nature and activity performed in construction. Here major role playing factor in success of a project is efficient scheduling of the construction project. While scheduling a project, the factors which are considered are mostly tangible, such as resources, labor and capital. My main objective in this research is to reduce the duration of the activity involved in construction, while considering qualitative factors like site organization, Labor Skills and quality of equipment used and reveal the effect of these tangible factors on duration of the project. Since these factors are difficult to be measured using classical mathematic theories. We have opted Fuzzy Logic Theory as our base, which can measure mathematically with use of Fuzzy Logic Tool Box in MATLAB®. In order to implement the proposed technique, various membership functions need to be estimated using judgement and guidance of experts. One of the main advantage of the proposed technique is that it can be easily implemented in existing computer programs and thus having a possibility to schedule a project efficiently

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested. Crown Copyright © 2008