This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF) model and fuzzy transform (F-transform), termed FT-LLNF, for prediction of energy consumption. LLNF models are powerful in modelling and forecasting highly nonlinear and complex time series. Starting from an optimal linear least square model, they add nonlinear neurons to the initial model as long as the model's accu...

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the...

As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locall...

in this study, detection and identification of common faults in industrial gas turbines is investigated. we propose a model-based robust fault detection(fd) method based on multiple models. for residual generation a bank of local linear neuro-fuzzy (llnf) models is used. moreover, in fault detection step, a passive approach based on adaptive threshold is employed. to achieve this purpose, the a...

The walking beam furnace (WBF) is one of the most prominent process plants often met in an alloy steel production factory and characterised by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence,...

For winning in global competition, companies need to recognition and monitoring of customer's behavior to forecast their behavior and desires earlier than competitors. This research tries to recognize the attributes which lead to customer churn. For this, behavior of 3150 subscribers of an Iranian mobile operator, has observed during one year and trends of them has analyzed by a customized LLNF...

In this study, detection and identification of common faults in industrial gas turbines is investigated. We propose a model-based robust fault detection(FD) method based on multiple models. For residual generation a bank of Local Linear Neuro-Fuzzy (LLNF) models is used. Moreover, in fault detection step, a passive approach based on adaptive threshold is employed. To achieve this purpose, the a...

this paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. the output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (llnf) model, is useful for assessing a project status at different time horizons. being trained by a locally linear model tree (lolimot) learning algorithm, the model is int...

for winning in global competition, companies need to recognition and monitoring of customer's behavior to forecast their behavior and desires earlier than competitors. this research tries to recognize the attributes which lead to customer churn. for this, behavior of 3150 subscribers of an iranian mobile operator, has observed during one year and trends of them has analyzed by a customized...

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...