Granular computing neural-fuzzy modelling: A neutrosophic approach

Abstract

Granular computing is a computational paradigm that mimics human cognition in terms of grouping similar information together. Compatibility operators such as cardinality, orientation, density, and multidimensional length act on both in raw data and information granules which are formed from raw data providing a framework for human-like information processing where information granulation is intrinsic. Granular computing, as a computational concept, is not new, however it is only relatively recent when this concept has been formalised computationally via the use of Computational Intelligence methods such as Fuzzy Logic and Rough Sets. Neutrosophy is a unifying field in logics that extents the concept of fuzzy sets into a three-valued logic that uses an indeterminacy value, and it is the basis of neutrosophic logic, neutrosophic probability, neutrosophic statistics and interval valued neutrosophic theory. In this paper we present a new framework for creating Granular Computing Neural-Fuzzy modelling structures via the use of Neutrosophic Logic to address the issue of uncertainty during the data granulation process. The theoretical and computational aspects of the approach are presented and discussed in this paper, as well as a case study using real industrial data. The case study under investigation is the predictive modelling of the Charpy Toughness of heat-treated steel; a process that exhibits very high uncertainty in the measurements due to the thermomechanical complexity of the Charpy test itself. The results show that the proposed approach leads to more meaningful and simpler granular models, with a better generalisation performance as compared to other recent modelling attempts on the same data set.

A real-time quality monitoring framework for steel friction stir welding using computational intelligence

“…A type2-fuzzy logic system (T2-FLS) is the same as a type1-fuzzy logic system (T1-FLS) [33][34][35], which is characterised by linguistic IF. .…”

“…This is the same MLP-NN modelling approach that was used in the study reported in [24]. The type-1 radial basis function neural network (T1-RBF-NN) has also five rules (for fair comparison purposes) and is trained via an adaptive error back propagation (BP) learning algorithm [33,34,48].…”

Information granularity model for evolving context-based fuzzy system

“…For example, ECSFS [11] and SSEM [10] models are error-reducing evolving methods, and boundary constraints are used in the evolving method. Consequently, many studies on fuzzy granular approach focus on the improvement of interpretability constraints (or decision boarders) to achieve a low model error [16,17,20,21,25]; this is a second significant consideration for granular computing. Therefore, evolving granule approach and interpretability constraint are important to coexist concurrently.…”

“…Furthermore, the concept of justifiable granularity and allocation of information granularity [16,17,20,21,25] consist of the fundamental blocks of granular computing. The optimal allocation of information granularity [21,48] can be employed to group decision making problems [22][23][24] in which the initial preferences from the decision maker can be adapted to reach higher agreement [48].…”

Fuzzy uncertainty assessment in RBF Neural Networks using neutrosophic sets for multiclass classification

“…This work is shown to improve the classification performance and to control the linguistic uncertainty produced throughout the construction of the inference mechanism. In [12], Solis and Panoutsos proposed an RBF-NN-based neutrosophic framework for the prediction of heat treated steel properties where a neutrosophic index was designed in order to measure the inclusion uncertainty throughout the granulation process used for estimating the parameters of the RBF-NN. Nevertheless, the design of logic-driven systems and interpretable models based on RUs has been an ongoing challenge in the area of modelling.…”