928 research outputs found
Perpetual Learning Framework based on Type-2 Fuzzy Logic System for a Complex Manufacturing Process
This paper introduces a perpetual type-2 Neuro-Fuzzy modelling structure for continuous learning and its application to the complex thermo-mechanical metal process of steel Friction Stir Welding (FSW). The ‘perpetual’ property refers to the capability of the proposed system to continuously learn from new process data, in an incremental learning fashion. This is particularly important in industrial/manufacturing processes, as it eliminates the need to retrain the model in the presence of new data, or in the case of any process drift. The proposed structure evolves through incremental, hybrid (supervised/unsupervised) learning, and accommodates new sample data in a continuous fashion. The human-like information capture paradigm of granular computing is used along with an interval type-2 neural-fuzzy system to develop a modelling structure that is tolerant to the uncertainty in the manufacturing data (common challenge in industrial/manufacturing data). The proposed method relies on the creation of new fuzzy rules which are updated and optimised during the incremental learning process. An iterative pruning strategy in the model is then employed to remove any redundant rules, as a result of the incremental learning process. The rule growing/pruning strategy is used to guarantee that the proposed structure can be used in a perpetual learning mode. It is demonstrated that the proposed structure can effectively learn complex dynamics of input-output data in an adaptive way and maintain good predictive performance in the metal processing case study of steel FSW using real manufacturing dat
Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting
Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods
EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification
Power-quality disturbances lead to several drawbacks such as limitation of
the production capacity, increased line and equipment currents, and consequent
ohmic losses; higher operating temperatures, premature faults, reduction of
life expectancy of machines, malfunction of equipment, and unplanned outages.
Real-time detection and classification of disturbances are deemed essential to
industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC)
framework for semi-supervised disturbance detection and classification combined
with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform
attribute-extraction method applied over a landmark window of voltage
waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory
transient are considered. Different from other monitoring systems, which
require offline training of models based on a limited amount of data and
occurrences, the proposed online data-stream-based EGFC method is able to learn
disturbance patterns autonomously from never-ending data streams by adapting
the parameters and structure of a fuzzy rule base on the fly. Moreover, the
fuzzy model obtained is linguistically interpretable, which improves model
acceptability. We show encouraging classification results.Comment: 10 pages, 6 figures, 1 table, IEEE International Conference on Fuzzy
Systems (FUZZ-IEEE 2020
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