Researchers introduce in this paper, an efficient fuzzy time series forecasting model based on fuzzy clustering to handle forecasting problems and improving forecasting accuracy. Each value (observation) is represented by a fuzzy set. The transition between consecutive values is taken into account in order to model the time series data. Proposed model employed eight main steps in time-invariant...

Prediction is a critical component in decision-making process for business management. Fuzzy Markov model is a common approach for dealing with the prediction of time series. However, not many studies devoted their attention to the effect of the parameters on model fitting for fuzzy Markov model. In the paper, we examine the prediction ability for fuzzy Markov model, based on the data of Taiwan...

This paper presents a novel fuzzy identification method for dynamic modelling of quadrotor UAVs. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remai...

Current fuzzy control research tries to obtain the less conservative conditions to prove stability and performance of fuzzy control systems. In many fuzzy models, membership functions with multiple arguments are defined as the product of simpler ones, where all possible combinations of such products conform a fuzzy partition. In particular, such situation arises with widely-used fuzzy modelling...

fuzzy rules based classifier systems (FRBS) have gained more popularity in recent years. This classifier suffers from the problem of pattern space partitioning, to reach a compact set of rules that provides high classification power. In this paper, we propose an adaptive hierarchical fuzzy partitioning method based on tree decomposition. This decomposition is controlled by the grade of certaint...

Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...

In this paper, the fuzzy neural network is selected as the algorithm for data mining (DM), introducing the artificial neural network into the fuzzy logic by treating it as a computing tool, it is a networklized description form by using the artificial neural network as the membership function in a fuzzy system, fuzzy rules and extension principle. The fuzzy neural network (FNN) is selected as t...

A novel framework is described for mining fuzzy Association Rules (fuzzy ARs) relating the properties of composite attributes, i.e. attributes or items that each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composit...

Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy a method uses concept logic, which Song and Chissom first introduced. Markov chain in defuzzification. determination length interval plays an important role forming logic relationship (FLR), this FLR will be used to determine value. One can average-based. However, several studies use partition...

The sensitivity of histogram computation to the choice of a reference interval and number of bins can be attenuated by replacing the crisp partition on which the histogram is built by a fuzzy partition. This involves replacing the crisp counting process by a distributed (weighted) voting process. The counterpart to this low sensitivity is some confusion in the count values: a value of 10 in the...